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, Borut Jug, Mitja Lainscak
Published: 29 March 2021
Frontiers in Physiology, Volume 12; https://doi.org/10.3389/fphys.2021.665568

Abstract:
A Commentary onCommentary: Blood Flow Restriction Exercise: Considerations of Methodology, Application, and Safetyby Spranger, M. D. (2020). Front. Physiol. 11:599592. doi: 10.3389/fphys.2020.599592 We read with great interest the recent comprehensive guidelines for the implementation of blood flow-restricted resistance exercise (BFR-RE) into sports and clinical practice. The authors provided an extensive description of the mechanism and training application and addressed many important safety considerations (Patterson et al., 2019). With the potential of BFR exercise expanded to clinical settings [such as in orthopedic (Hughes et al., 2017) and cardiovascular patients (Madarame et al., 2013; Tanaka and Takarada, 2018; Kambič et al., 2019)], many previous reviews have raised safety concerns (Spranger et al., 2015; Oliveira et al., 2019). These are due to potential peripheral ischemia-induced hyperactivity of III and IV nerve afferents that could evoke muscle metabo- and/or mechanoreflex (e.g., the exercise pressor reflex), primarily in cardiovascular patients (Piepoli et al., 2008; Angius and Crisafulli, 2020). Since the guidelines focused mainly on the effects of BFR-RE on cardiovascular response and blood coagulation (Patterson et al., 2019), the recent commentary in the journal also highlighted this important safety issue (Spranger, 2020). In the commentary, the potential role of exercise pressor reflex during BFR-RE was linked with a higher increase in blood pressure during low-load BFR-RE compared with low-load RE without BFR (Spranger, 2020), as demonstrated in previous studies enrolling healthy adults (Downs et al., 2014; Hori et al., 2020) and older women (Scott et al., 2018). In contrast, one study, not included in the recent commentary (Spranger, 2020), has demonstrated lower blood pressure during low-load BFR-RE [30% of one-repetition maximum (1-RM), 4 sets of 15 repetitions per set, with 60 s of rest between sets] compared with low-load and high-load RE to failure (Libardi et al., 2017). This indicates that time under BFR is likely a major contributor to more pronounced exercise pressor reflex observed when sets of more than 15 repetitions (Scott et al., 2018; Hori et al., 2020) or sets to volitional fatigue are performed (Downs et al., 2014). Therefore, we agree with the author that future BFR-RE training implementations in cardiovascular rehabilitation settings should take into consideration the duration of time under BFR (e.g., duration of the exercise), applied cuff pressure to the limb, and width of the cuff (Loenneke et al., 2013), as the main mediators of the magnitude of exercise pressor response (Oliveira et al., 2019). The implementation of BFR-RE in cardiovascular patients (e.g., coronary artery disease, heart failure, and peripheral artery disease) was addressed only in two hemodynamic studies (Pinto and Polito, 2016; Kambič et al., 2020). Both were included in the commentary (Spranger, 2020), yet we argue that several key findings of our study about BFR-RE safety (e.g., hemodynamic response during exercise) were not discussed thoroughly. Importantly, our study also measured hemodynamic response during low-load BFR-RE at 30 and 40% of 1-RM (Kambič et al., 2020), in addition to the already mentioned hemodynamic adaptations after BFR resistance training (RT) (Kambič et al., 2019). Prior to BFR-RT intervention, we measured heart rate and blood pressure response to three sets of 8, 10, and 12 repetitions at the intensity of 30% of 1-RM, a lifting cadence of 1 s of concentric contraction and 2 s of eccentric contraction, and with 45 s of rest between sets (Kambič et al., 2020). Heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP) increased significantly in the first set (HR: +10 bpm, SBP: +12 mmHg, DBP: +3 mmHg), second set (HR: +14 bpm, SBP: +22 mmHg, DBP: +10 mmHg), and third set (HR: +18 bpm, SBP: +13 mmHg, DBP: +3 mmHg) compared with baseline levels. Furthermore, HR, SBP, and DBP increased significantly from the second set to the third set, while BP was significantly lower after the cuff pressure was released after the third set compared with the second set. All hemodynamic parameters returned to baseline values after the end of BFR-RE. After the completion of 8 weeks of BFR-RT intervention, we re-evaluated the hemodynamic response to BFR-RE at the intensity of 40% of 1-RM. With the exception of lower diastolic pressure in the third set compared with the first of the BFR-RE, leading to a significant set × intensity interaction (p = 0.027), we observed a similar increase in HR and SBP as during the baseline measurement at 30% of 1-RM, with no significant set × intensity interaction. In addition, BFR-RE did not induce any changes in circulating levels of hemostatic markers (D-dimer and fibrinogen) and N-terminal prohormone B-type natriuretic hormone following acute BFR-RE and BFR-RT (Kambič et al., 2020), which is in line with the only study available in coronary artery disease patients (Madarame et al., 2013). Despite our novel findings on the safety and efficacy of BFR-RT on muscle strength and vascular function, there remain many methodological limitations and unanswered questions. These should be addressed in future trials before BFR-RE can be routinely included in cardiac rehabilitation. Ideally, future trials should use indirect beat-by-beat methods (photoplethysmography or impendance cardiography) (Downs et al., 2014; Scott et al., 2018) or direct measurements of hemodynamic response using an arterial and venous catheter on the exercising limb (Franz et al., 2020); as these methods are not used routinely, a correlation study with usual hemodynamic monitors (automated BP monitor) should be considered. Future trials should also be designed to study the hemodynamic effects of high-load RE (>70% 1-RM) and low-load RE with and without BFR (<40% 1-RM). In addition, special consideration should be given to the selection of narrow cuffs (Loenneke et al., 2013) and the reduction of time under BFR, with manipulation of the number of sets (<3–4 sets) and repetitions (<15 repetitions per set) (Madarame et al., 2013; Kambič et al., 2019), and lifting cadence (1 s:1 s of concentric and eccentric contraction) (Lamotte et al., 2010) to minimize the (potential) activation of exercise pressor reflex in cardiovascular disease patients. TK: writing of the manuscript draft and responsible for the final content. BJ and ML: writing of the manuscript draft. All authors read, critically reviewed, and approved the final version of the manuscript. This work was funded by the Slovenian Research Agency (research grant no. J3-9292, Burden of cachexia and sarcopenia in patients with chronic diseases: epidemiology, pathophysiology, and outcomes, and research grant no. J3-9284, Epidemiology, pathophysiology, and clinical relevance of anemia in chronic cardiopulmonary patients). TK received a research fellowship from the Slovenian Research Agency (grant no. 630-72/2019-1). The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Angius, L., and Crisafulli, A. (2020). Exercise intolerance and fatigue in chronic heart failure: is there a role for group III/IV afferent feedback? Eur. J. Prev. Cardiol. 27, 1862–1872. doi: 10.1177/2047487320906919 PubMed Abstract | CrossRef Full Text | Google Scholar Downs, M. E., Hackney, K. J., Martin, D., Caine, T. L., Cunningham, D., O'Connor, D. P., et al. (2014). Acute vascular and cardiovascular responses to blood flow–restricted exercise. Med. Sci. Sports Exerc. 46, 1489–1497. doi: 10.1249/MSS.0000000000000253 PubMed Abstract | CrossRef Full Text | Google Scholar Franz, A., Berndt, F., Raabe, J., Harmsen, J.-F., Zilkens, C., and Behringer, M. (2020). Invasive assessment of hemodynamic, metabolic and ionic consequences during blood flow restriction training. Front. Physiol.11:617668. doi: 10.3389/fphys.2020.617668 PubMed Abstract | CrossRef Full Text | Google Scholar Hori, A., Hasegawa, D., Suijo, K., Nishigaki, K., Ishida, K., and Hotta, N. (2020). Exaggerated pressor response to blood flow restriction resistance exercise is associated with a muscle metaboreflex-induced increase in blood pressure in young, healthy humans. Appl. Physiol. Nutr. Metabol. 46, 182–185. doi: 10.1139/apnm-2020-0491 PubMed Abstract | CrossRef Full Text | Google Scholar Hughes, L., Paton, B., Rosenblatt, B., Gissane, C., and Patterson, S. D. (2017). Blood flow restriction training in clinical musculoskeletal rehabilitation: a systematic review and meta-analysis. Br. J. Sports Med. 51, 1003–1011. doi: 10.1136/bjsports-2016-097071 PubMed Abstract | CrossRef Full Text | Google Scholar Kambič, T., Novaković, M., Tomažin, K., Strojnik, V., Božič-Mijovski, M., and Jug, B. (2020). Hemodynamic and hemostatic response to blood flow restriction resistance exercise in coronary artery disease. J. Cardiovasc. Nurs. doi: 10.1097/JCN.0000000000000699. [Epub ahead of print]. PubMed Abstract | CrossRef Full Text | Google Scholar Kambič, T., Novaković, M., Tomažin, K., Strojnik, V., and Jug, B. (2019). Blood flow restriction resistance exercise improves muscle strength and hemodynamics, but not vascular function in coronary artery disease patients: a pilot randomized controlled trial. Front. Physiol. 10:656. doi: 10.3389/fphys.2019.00656 PubMed Abstract | CrossRef Full Text | Google Scholar Lamotte, M., Fleury, F., Pirard, M., Jamon, A., Borne, P., and van, de. (2010). Acute cardiovascular response to resistance training during cardiac rehabilitation: effect of repetition speed and rest periods. Eur. J. Cardiovasc. Prev. Rehabil. 17, 329–336. doi: 10.1097/HJR.0b013e328332efdd PubMed Abstract | CrossRef Full Text | Google Scholar Libardi, C. A., Catai, A. M., Miquelini, M., Borghi-Silva, A., Minatel, V., Alvarez, I., et al. (2017). Hemodynamic responses to blood flow restriction and resistance exercise to muscular failure. Int. J. Sports Med. 38, 134–140. doi: 10.1055/s-0042-115032 PubMed Abstract | CrossRef Full Text | Google Scholar Loenneke, J., Fahs, C., Rossow, L., Thiebaud, R., Mattocks, K., Abe, T., et al. (2013). Blood flow restriction pressure recommendations: a tale of two cuffs. Front. Physiol. 4:249. doi: 10.3389/fphys.2013.00249 PubMed Abstract | CrossRef Full Text | Google Scholar Madarame, H., Kurano, M., Fukumura, K., Fukuda, T., and Nakajima, T. (2013). Haemostatic and inflammatory responses to blood flow-restricted exercise in patients with ischaemic heart disease: A pilot study. Clin. Physiol. Funct. Imaging 33, 11–17. doi: 10.1111/j.1475-097X.2012.01158.x PubMed Abstract | CrossRef Full Text | Google Scholar Oliveira, M., Meireles, K., Spranger, M. D., O'Leary, D. S., Roschel, H., and Peçanha, T. (2019). Clinical safety of blood flow-restricted training? A comprehensive review of altered muscle metaboreflex in cardiovascular disease during ischemic exercise. Am. J. Physiol. Heart Circ. Physiol. 318, H90–H109. doi: 10.1152/ajpheart.00468.2019 PubMed Abstract | CrossRef Full Text | Google Scholar Patterson, S. D., Hughes, L., Warmington, S., Burr, J., Scott, B. R., Owens, J., et al. (2019). Blood flow restriction exercise: considerations of methodology, application, and safety. Front. Physiol. 10:533. doi: 10.3389/fphys.2019.00533 CrossRef Full Text | Google Scholar Piepoli, M. F., Dimopoulos, K., Concu, A., and Crisafulli, A. (2008). Cardiovascular and ventilatory control during exercise in chronic heart failure: role of muscle reflexes. Int. J. Cardiol. 130, 3–10. doi: 10.1016/j.ijcard.2008.02.030 PubMed Abstract | CrossRef Full Text | Google Scholar Pinto, R. R., and Polito, M. D. (2016). Haemodynamic responses during resistance exercise with blood flow restriction in hypertensive subjects. Clin. Physiol. Funct. Imaging 36, 407–413. doi: 10.1111/cpf.12245 PubMed Abstract | CrossRef Full Text | Google Scholar Scott, B. R., Peiffer, J. J., Thomas, H. J., Marston, K. J., and Hill, K. D. (2018). Hemodynamic responses to low-load blood flow restriction and unrestricted high-load resistance exercise in older women. Front. Physiol. 9:1324. doi: 10.3389/fphys.2018.01324 CrossRef Full Text | Google Scholar Spranger, M. D. (2020). Commentary: blood flow restriction exercise: considerations of methodology, application, and safety. Front. Physiol. 11:599592. doi: 10.3389/fphys.2020.599592 PubMed Abstract | CrossRef Full Text | Google Scholar Spranger, M. D., Krishnan, A. C., Levy, P. D., O'Leary, D. S., and Smith, S. A. (2015). Blood flow restriction training and the exercise pressor reflex: a call for concern. Am. J. Physiol. Heart Circ. Physiol. 309, H1440–H1452. doi: 10.1152/ajpheart.00208.2015 PubMed Abstract | CrossRef Full Text | Google Scholar Tanaka, Y., and Takarada, Y. (2018). The impact of aerobic exercise training with vascular occlusion in patients with chronic heart failure. ESC Heart Fail. 5, 586–591. doi: 10.1002/ehf2.12285 PubMed Abstract | CrossRef Full Text | Google Scholar Keywords: blood flow restriction, resistance training, exercise pressor reflex, coronary artery disease, cardiac rehabilitation Citation: Kambic T, Jug B and Lainscak M (2021) Response: Commentary: Blood Flow Restriction Exercise: Considerations of Methodology, Application, and Safety. Front. Physiol. 12:665568. doi: 10.3389/fphys.2021.665568 Received: 08 February 2021; Accepted: 26 February 2021; Published: 29 March 2021. Edited by: Reviewed by: Copyright © 2021 Kambic, Jug and Lainscak. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Tim Kambic, [email protected] These authors have contributed equally to this work and share senior authorship
Dean W. A. Walton, Kiran T. Thakur, Arun Venkatesan, Gerome Breen, Tom Solomon,
Published: 10 February 2021
Frontiers in Neurology, Volume 12; https://doi.org/10.3389/fneur.2021.637586

Abstract:
Over a century since the H1N1 influenza pandemic of 1918, we are in the midst of another global pandemic: COVID-19, caused by Severe Acute Respiratory Syndrome- Coronavirus−2 (SARS-CoV-2). While predominantly a respiratory illness, evidence of neurological conditions is arising and we are seeing a plethora of heterogeneous neurology with COVID-19. Analysis of past communicable disease outbreaks and contemporaneous reports will allow us to better understand the potential role of direct neuroinvasion. Encephalitis has been observed in previous viral pandemics and epidemics as well as seasonal Coronavirus outbreaks in rare cases. In three cases of encephalopathy with seizures Severe Acute Respiratory Syndrome- Coronavirus-1 (SARS-CoV-1) was identified through brain culture and reverse-transcription polymerase chain reaction (RT-PCR) of CSF (1–3). Three cases of Middle Eastern Respiratory Syndrome (MERS) displayed features of Acute Disseminated Encephalomyelitis (ADEM) and Bickerstaff's Encephalitis but did not find CSF evidence of viral nucleic acid by RT-PCR (4, 5). During the Influenza A pandemic of 2009 (H1N1), cases of Influenza-associated encephalopathy (IAE) increased nearly 7-fold compared to the average over the previous five seasons and seizures and encephalopathy were a common initial presentation among children (6–8). Furthermore, a seasonal coronavirus (OC43) is documented to have caused encephalitis in two immunodeficient children (9, 10). Lastly, although never proven, the 1918 pandemic caused by the H1N1 virus has been associated with the wave of encephalitis lethargica observed at the time (11). Given this history and these significant findings it is perhaps not surprising that we are seeing a multitude of neurological sequelae associated with the COVID-19 pandemic. However, correlation does not equal causation, and the challenge is distinguishing between neurological complications secondary to critical illness and those directly linked to the virus itself (12). The encephalitis syndromes seen with COVID-19 are heterogenous in their presentation (13). This undoubtedly represents varied underlying neuropathogenesis. Acute presentations are potentially a consequence of systemic pro-inflammatory cytokines transcending the blood-brain barrier (BBB) or due to direct viral invasion of the central nervous system (CNS) in a small number of cases (12, 14). Later, post-infectious presentations are more likely to be due to immune mediated processes operating through cellular or antibody pathways (6, 15). Since the first case of COVID-19 encephalitis was reported (16), in whom SARS-CoV-2 RNA was detected in CSF but not on nasopharyngeal RT-PCR, several other case studies have corroborated this phenomenon and demonstrated potential viral invasion as the cause in these cases by positive SARS-CoV-2 RT-PCR in CSF and tissue samples, and evidence of viral particles in neural cell bodies (1, 17, 18). A review article of 21 case reports, found that in 10 patients with proposed encephalitis in whom CSF RT-PCR for SARS-CoV-2 was performed, four were positive and the majority also tested positive for nasopharyngeal RNA (19). The review was critical of attributing symptomology to parenchymal invasion and support for this skepticism also comes from post-mortem data. A study of 43 patients observed marked neuroinflammation in the brainstem of COVID-19 patients at post-mortem with microglia activation and cytotoxic T cell infiltration (20). However, only 21 had evidence of SARS-CoV-2 RNA in post-mortem tissue by PCR and this was not associated with the severity of inflammatory histopathological changes. Moreover, detection of viral RNA in CNS tissue may reflect virus in the blood vessels of cerebral vasculature (as most tissues will have not undergone whole body perfusion via the left ventricle as would be undertaken in murine models) or passive viral entry through a disrupted BBB. Indeed, there is increasing evidence of BBB disruption in COVID-19 (21, 22). In addition to representing an alternative viral entry pathway to the CNS these presentations may represent systemic pro-inflammatory cytokines affecting the CNS, and hence para-infectious, inflammation and maybe also post-infectious antibody response directed against CNS antigens (12). A further case series of eight patients that examined SARS-CoV-2 antibody (Ab) titres in CSF and serum of COVID-19 patients found detectable SARS-CoV-2 Ab in the CSF of all eight patients but these samples were negative for SARS-CoV-2 by PCR (21). They also demonstrated high CSF titres comparable to serum in four of these patients and evidence of intrathecal synthesis in one patient. This scarcity of detecting SARS-CoV-2 by RT-PCR in CSF of encephalitis cases may reflect disease mechanisms other than direct invasion but alternatively could question the sensitivity of the test itself. Taken together, these findings highlight the importance of testing the CSF by both PCR and IgG/IgM, and of interpreting antibody findings relative to the concurrent serum titer and to the CSF:serum albumin ratio using Rieber's formula to confirm true intrathecal synthesis. Even prior to COVID-19, many cases of viral encephalitis were well-recognized to be negative in CSF by PCR but antibody positive, such as West Nile virus, and many other flaviviridae and coronaviridea. Nevertheless, in emerging zoonotic infections, such as SARS-CoV-2, in which the pathophysiology is in question, direct visualization of the virus, such as with fluorescent in-situ hybridization, remains the gold standard for confirming encephalitis with direct viral neurotropic invasion. There are now multiple case reports related to post-infectious phenomenon, such as ADEM (17, 23), limbic encephalitis (13, 24), autoimmune encephalitis (25, 26), and in some cases specific autoantibodies directed against CNS antigens have been identified (27). In the face of amassing evidence of encephalitis in COVID-19, rigorous critique of these case reports and series is needed as several studies lack vital investigations and report diagnoses with minimal evidence (13, 19). However, a common finding among multiple studies is that the presence of neurological complications in COVID-19 has a negative impact on outcomes and delays recovery, although the long-term impact of these complications is not yet known and whether delayed emergent, post-infectious, complications develop is unclear (28–30). As SARS-CoV-2 continues its unrelenting march we will undeniably see further evidence of neurological complications. Currently, while encephalitis cases are sparse it is pertinent that subsequent cases are all identified and meticulously documented in order to classify neurological sequelae in COVID-19. Standardized diagnostic frameworks, such as that proposed by Ellul et al., which utilize the World Health Organization COVID-19 case definitions and apply them to cover neurological clinical syndromes will be valuable for international comparisons of reported cases, case series, and cohort studies (11). In particular, it is important to distinguish between non-specific symptoms associated with critical illness regardless of etiology and those linked with SARS-CoV-2 directly or indirectly (12). Once classified, consolidating cases and the information they provide is imperative to draw comparisons, appreciate patterns and better understand the neuropathogenesis of COVID-19. Already there are examples of national observational, prospective multi-center, prospective cohort studies (15, 28, 31) that are evaluating the prevalence and outcomes of neurological complications in COVID-19. Efforts to systematically categorize and analyse all relevant publications on a weekly basis is also underway (32). Furthermore, national surveillance programmes that collect and collate neurological cases by allowing clinicians to easily and quickly identify patients in real time are powerful tools for timely appreciation of potential neurological complications of COVID-19; and those which span the clinical neurosciences—including psychiatry and neurosurgery—are of particular value when combating such a rapidly progressing global threat (33). Lastly, on-going research concerning longer term sequelae and underlying biology is necessary (34–36), especially since the H1N1 pandemic was followed by a wave of encephalitis lethargica that put a considerable strain on health services (37). While the end of this current pandemic often feels distant, we are hopeful that in the coming months more evidence will be accumulated for potential vaccine candidates. When this pandemic is over, we will need to prepare for future pandemics and epidemics, and ask ourselves- what can we learn from our experience of COVID-19's effects on the brain? In order to become more proficient and expedient at addressing viral outbreaks we must learn from current events; what has been done well in addition to shortcomings (36). Rapid and early identification of novel zoonoses or mutations is vital to limit spread; containment alone however may prove difficult, as demonstrated by SARS-CoV-2. Once a new threat has reached pandemic proportions, emphasis should turn toward international collaborative efforts of identification, classification, and knowledge sharing, especially with neurological complications where caseloads may be smaller (36, 38). It is only by implementing a worldwide collaborative response across the neuroscience community that we can tackle such a global disease. Even though this task appears daunting, with the tireless efforts of scientists, medical professionals and researchers what we learn now from this pandemic, even more than in previous pandemics, has the potential for us to be much better prepared for those pandemics yet to come. The final draft was written, reviewed, and approved by all authors. KT was supported by NINDS/NIH 1K23NS105935-01. AV was supported by research grants from the NIH, Maryland Stem Cell Research Fund, and U.S. Department of Defense. TS was supported by the European Union's Horizon 2020 research and innovation programme, ZikaPLAN (Preparedness Latin America Network; grant 734584), the National Institute for Health Research (NIHR) Global Health Research Group on Brain Infections (17/63/110) and the NIHR Health Protection Research Unit in Emerging and Zoonotic Infections at University of Liverpool. BM was supported by grants from the UKRI/MRC (COVID-CNS; MR/V03605X/1), Medical Research Council, Wellcome, and NIHR. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 1. Hung EC, Chim SS, Chan PK, Tong YK, Ng EK, Chiu RW, et al. Detection of SARS coronavirus RNA in the cerebrospinal fluid of a patient with severe acute respiratory syndrome. Clin Chem. (2003) 49:2108–9. doi: 10.1373/clinchem.2003.025437 PubMed Abstract | CrossRef Full Text | Google Scholar 2. Lau KK, Yu WC, Chu CM, Lau ST, Sheng B, Yuen KY. Possible central nervous system infection by SARS coronavirus. Emerg Infect Dis. (2004) 10:342–4. doi: 10.3201/eid1002.030638 PubMed Abstract | CrossRef Full Text | Google Scholar 3. Xu J, Zhong S, Liu J, Li L, Li Y, Wu X, et al. Detection of severe acute respiratory syndrome coronavirus in the brain: potential role of the chemokine mig in pathogenesis. Clin Infect Dis. (2005) 41:1089–96. doi: 10.1086/444461 PubMed Abstract | CrossRef Full Text | Google Scholar 4. Arabi YM, Harthi A, Hussein J, Bouchama A, Johani S, Hajeer AH, et al. Severe neurologic syndrome associated with Middle East respiratory syndrome corona virus (MERS-CoV). Infection. (2015) 43:495–501. doi: 10.1007/s15010-015-0720-y PubMed Abstract | CrossRef Full Text | Google Scholar 5. Kim JE, Heo JH, Kim HO, Song SH, Park SS, Park TH, et al. Neurological complications during treatment of middle east respiratory syndrome. J Clin Neurol. (2017) 13:227–33. doi: 10.3988/jcn.2017.13.3.227 PubMed Abstract | CrossRef Full Text | Google Scholar 6. Goenka A, Michael BD, Ledger E, Hart IJ, Absoud M, Chow G, et al. Neurological manifestations of influenza infection in children and adults: results of a National British Surveillance Study. Clin Infect Dis. (2014) 58:775–84. doi: 10.1093/cid/cit922 PubMed Abstract | CrossRef Full Text | Google Scholar 7. Gu Y, Shimada T, Yasui Y, Tada Y, Kaku M, Okabe N. National surveillance of influenza-associated encephalopathy in Japan over six years, before and during the 2009-2010 influenza pandemic. PLoS ONE. (2013) 8:e54786. doi: 10.1371/journal.pone.0054786 PubMed Abstract | CrossRef Full Text | Google Scholar 8. Surana P, Tang S, McDougall M, Tong CY, Menson E, Lim M. Neurological complications of pandemic influenza A H1N1 2009 infection: European case series and review. Eur J Pediatr. (2011) 170:1007–15. doi: 10.1007/s00431-010-1392-3 PubMed Abstract | CrossRef Full Text | Google Scholar 9. Morfopoulou S, Brown JR, Davies EG, Anderson G, Virasami A, Qasim W, et al. Human coronavirus OC43 associated with fatal encephalitis. N Engl J Med. (2016) 375:497–8. doi: 10.1056/NEJMc1509458 PubMed Abstract | CrossRef Full Text | Google Scholar 10. Yeh EA, Collins A, Cohen ME, Duffner PK, Faden H. Detection of coronavirus in the central nervous system of a child with acute disseminated encephalomyelitis. Pediatrics. (2004) 113:e73–6. doi: 10.1542/peds.113.1.e73 CrossRef Full Text | Google Scholar 11. Ellul MA, Benjamin L, Singh B, Lant S, Michael BD, Easton A, et al. Neurological associations of COVID-19. Lancet Neurol. (2020) 19:767–83. doi: 10.1016/S1474-4422(20)30221-0 CrossRef Full Text | Google Scholar 12. Ellul M, Varatharaj A, Nicholson TR, Pollak TA, Thomas N, Easton A, et al. Defining causality in COVID-19 and neurological disorders. J Neurol Neurosurg Psychiatry. (2020) 91:811–2. doi: 10.1136/jnnp-2020-323667 PubMed Abstract | CrossRef Full Text | Google Scholar 13. Pilotto A, Masciocchi S, Volonghi I, Crabbio M, Magni E, De Giuli V, et al. Clinical presentation and outcomes of SARS-CoV-2 related encephalitis: the ENCOVID multicentre study. J Infect Dis. (2020) 223:28–37. doi: 10.1093/infdis/jiaa609 CrossRef Full Text | Google Scholar 14. Guo Y, Korteweg C, McNutt MA, Gu J. Pathogenetic mechanisms of severe acute respiratory syndrome. Virus Res. (2008) 133:4–12. doi: 10.1016/j.virusres.2007.01.022 PubMed Abstract | CrossRef Full Text | Google Scholar 15. Varatharaj A, Thomas N, Ellul MA, Davies NWS, Pollak TA, Tenorio EL, et al. Neurological and neuropsychiatric complications of COVID-19 in 153 patients: a UK-wide surveillance study. Lancet Psychiatry. (2020) 7:875–82. doi: 10.2139/ssrn.3601761 PubMed Abstract | CrossRef Full Text | Google Scholar 16. Moriguchi T, Harii N, Goto J, Harada D, Sugawara H, Takamino J, et al. A first case of meningitis/encephalitis associated with SARS-Coronavirus-2. Int J Infect Dis. (2020) 94:55–8. doi: 10.1016/j.ijid.2020.03.062 PubMed Abstract | CrossRef Full Text | Google Scholar 17. Novi G, Rossi T, Pedemonte E, Saitta L, Rolla C, Roccatagliata L, et al. Acute disseminated encephalomyelitis after SARS-CoV-2 infection. Neurol Neuroimmunol Neuroinflamm. (2020) 7:e797. doi: 10.1212/NXI.0000000000000797 CrossRef Full Text | Google Scholar 18. Paniz-Mondolfi A, Bryce C, Grimes Z, Gordon RE, Reidy J, Lednicky J, et al. Central nervous system involvement by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). J Med Virol. (2020) 92:699–702. doi: 10.1002/jmv.25915 PubMed Abstract | CrossRef Full Text | Google Scholar 19. Pouga L. Encephalitic syndrome and anosmia in COVID-19: do these clinical presentations really reflect SARS-CoV-2 neurotropism? A theory based on the review of 25 COVID-19 cases. J Med Virol. (2020) 16:10. doi: 10.1002/jmv.26309 PubMed Abstract | CrossRef Full Text | Google Scholar 20. Matschke J, Lütgehetmann M, Hagel C, Sperhake JP, Schröder AS, Edler C, et al. Neuropathology of patients with COVID-19 in Germany: a post-mortem case series. Lancet Neurol. (2020) 19:919–29. doi: 10.1016/S1474-4422(20)30308-2 PubMed Abstract | CrossRef Full Text | Google Scholar 21. Alexopoulos H, Magira E, Bitzogli K, Kafasi N, Vlachoyiannopoulos P, Tzioufas A, et al. Anti-SARS-CoV-2 antibodies in the CSF, blood-brain barrier dysfunction, and neurological outcome: studies in 8 stuporous and comatose patients. Neurol Neuroimmunol Neuroinflamm. (2020) 7:e893. doi: 10.1212/NXI.0000000000000893 PubMed Abstract | CrossRef Full Text | Google Scholar 22. Buzhdygan TP, DeOre BJ, Baldwin-Leclair A, Bullock TA, McGary HM, Khan JA, et al. The SARS-CoV-2 spike protein alters barrier function in 2D static and 3D microfluidic in-vitro models of the human blood-brain barrier. Neurobiol Dis. (2020) 146:105131. doi: 10.1016/j.nbd.2020.105131 PubMed Abstract | CrossRef Full Text | Google Scholar 23. Parsons T, Banks S, Bae C, Gelber J, Alahmadi H, Tichauer M. COVID-19-associated acute disseminated encephalomyelitis (ADEM). J Neurol. (2020) 267:2799–802. doi: 10.1007/s00415-020-09951-9 PubMed Abstract | CrossRef Full Text | Google Scholar 24. Zambreanu L, Lightbody S, Bhandari M, Hoskote C, Kandil H, Houlihan CF, et al. A case of limbic encephalitis associated with asymptomatic COVID-19 infection. J Neurol Neurosurg Psychiatry. (2020) 91:1229–30. doi: 10.1136/jnnp-2020-323839 PubMed Abstract | CrossRef Full Text | Google Scholar 25. Grimaldi S, Lagarde S, Harle JR, Boucraut J, Guedj E. Autoimmune encephalitis concomitant with SARS-CoV-2 infection: insight from 18F-FDG PET imaging and neuronal autoantibodies. J Nucl Med. (2020) 61:1726–9. doi: 10.2967/jnumed.120.249292 PubMed Abstract | CrossRef Full Text | Google Scholar 26. Khoo A, McLoughlin B, Cheema S, Weil RS, Lambert C, Manji H, et al. Postinfectious brainstem encephalitis associated with SARS-CoV-2. J Neurol Neurosurg Psychiatry. (2020) 91:1013–4. doi: 10.1136/jnnp-2020-323816 CrossRef Full Text | Google Scholar 27. Pinto AA, Carroll LS, Nar V, Varatharaj A, Galea I. CNS inflammatory vasculopathy with antimyelin oligodendrocyte glycoprotein antibodies in COVID-19. Neurol Neuroimmunol Neuroinflamm. (2020) 7:e813. doi: 10.1212/NXI.0000000000000813 PubMed Abstract | CrossRef Full Text | Google Scholar 28. Frontera JA, Sabadia S, Lalchan R, Fang T, Flusty B, Millar-Vernetti P, et al. A prospective study of neurologic disorders in hospitalized COVID-19 patients in New York City. Neurology. (2020) 491:1–11. doi: 10.1212/WNL.0000000000010979 CrossRef Full Text | Google Scholar 29. Helms J, Kremer S, Merdji H, Schenck M, Severac F, Clere-Jehl R, et al. Delirium and encephalopathy in severe COVID-19: a cohort analysis of ICU patients. Crit Care. (2020) 24:491. doi: 10.1186/s13054-020-03200-1 CrossRef Full Text | Google Scholar 30. Shah VA, Nalleballe K, Zaghlouleh ME, Onteddu S. Acute encephalopathy is associated with worse outcomes in COVID-19 patients. Brain Behav Immun Health. (2020) 8:100136. doi: 10.1016/j.bbih.2020.100136 PubMed Abstract | CrossRef Full Text | Google Scholar 31. Koh JS, De Silva DA, Quek AML, Chiew HJ, Tu TM, Seet CYH, et al. Neurology of COVID-19 in Singapore. J Neurol Sci. (2020) 418:117118. doi: 10.1016/j.jns.2020.117118 CrossRef Full Text | Google Scholar 32. Journal of Neurology Neurosurgery & Psychiatry Blog. The Neurology and Neuropsychiatry of COVID-19. (2020). Available online at: https://blogs.bmj.com/jnnp/2020/05/01/the-neurology-and-neuropsychiatry-of-covid-19/ (accessed October 24, 2020). 33. CoroNerve Studies. CoroNerve Studies Group. (2020). Available online at: https://www.coronerve.com/ (accessed October 24, 2020). 34. PHOSP-COVID. PHOSP-COVID. (2020). Available online at: https://www.phosp.org/ (accessed October 24, 2020). 35. University of Liverpool. New National Study Into Neurological Impact of COVID-19. (2020) (accessed October 24, 2020) 36. Sigfrid L, Maskell K, Bannister PG, Ismail SA, Collinson S, Regmi S, et al. Addressing challenges for clinical research responses to emerging epidemics and pandemics: a scoping review. BMC Med. (2020) 18:190. doi: 10.1186/s12916-020-01624-8 PubMed Abstract | CrossRef Full Text | Google Scholar 37. Bramwell E, Miller J. Encephalitis lethargica (epidemic encephalitis). Lancet. (1920) 195:1152–8. doi: 10.1016/S0140-6736(00)92412-7 CrossRef Full Text | Google Scholar 38. Winkler AS, Knauss S, Schmutzhard E, Leonardi M, Padovani A, Abd-Allah F, et al. A call for a global COVID-19 neuro research coalition. Lancet Neurol. (2020) 19:482–4. doi: 10.1016/S1474-4422(20)30150-2 PubMed Abstract | CrossRef Full Text | Google Scholar Keywords: pandemic, encephalitis, COVID-19, SARS-COV-2, coronavirus Citation: Walton DWA, Thakur KT, Venkatesan A, Breen G, Solomon T and Michael BD (2021) Encephalitis in a Pandemic. Front. Neurol. 12:637586. doi: 10.3389/fneur.2021.637586 Received: 03 December 2020; Accepted: 21 January 2021; Published: 10 February 2021. Edited by: Reviewed by: Copyright © 2021 Walton, Thakur, Venkatesan, Breen, Solomon and Michael. 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, Maria Varkanitsa, Georgia Angelopoulou, Ioannis Evdokimidis, Dionysis Goutsos, Constantin Potagas
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Since the pioneering work of Paul Broca and Carl Wernicke, it has become clear that the interaction of aphasia research and theoretical linguistics can be beneficial for both disciplines: (1) in order to understand the nature of aphasia as a language disorder, it is crucial to understand the nature of language; its internal rules and principles, (2) linguistic analysis of aphasic speech can also provide some evidence on the relation between brain and language, (3) neurolinguistic data can be used to distinguish between competing linguistic theories, and (4) linguistic analysis of aphasic speech often leads to the design of linguistic-specific treatment programs for aphasia (for more details, see Avrutin, 2001). One of the most exciting recent developments in linguistics has been the widespread use of electronic corpora, both as a methodology and a theoretical viewpoint on language (see e.g., McEnery and Hardie, 2012, for an overview). In parallel, in aphasia research, large-scale data collection and group studies allow generalizations about the population from which the participants have been drawn, leading to useful findings (see Grodzinsky et al., 1999) that can complement single case studies, which allow for a detailed description of aphasic speech patterns and inferences about the language system in non-brain damaged individuals (see amongst others Badecker and Caramazza, 1985; Caramazza, 1986; Caramazza and Badecker, 1991). However, recruiting patients with aphasia on a large scale is difficult. Even when permission for collecting and using data by patients with aphasia has been obtained, considerable resources are required to move patients through the steps of consenting, screening and testing. A solution to this problem could be data sharing, as is increasingly realized in recent bibliography, which has evidenced a surge in corpora of language datasets from speakers with various disorders, including aphasia, in several languages such as Dutch (Westerhout and Monachesi, 2007), Cantonese (Kong and Law, 2019), Russian (Khudyakova et al., 2016), Croatian (Kuvač Kraljević et al., 2017), and, of course, English (Mirman et al., 2010; Williams et al., 2010; MacWhinney et al., 2011; Laures-Gore et al., 2016). Despite such attempts of developing corpora widely available to researchers, the need for additional open data banks from different languages still remains. For instance, for Greek a recent study has presented a detailed methodology for the transcription and annotation of aphasic speech samples (Varlokosta et al., 2016); although the authors describe an elaborate pipeline, no data has been available yet. Apart from the importance of data sharing discussed above, there is a methodological issue related to aphasic discourse analysis that is worth mentioning, namely, the method of eliciting a speech sample, which will be then used to evaluate a patient's linguistic competence on the basis of several indices, such as type and frequency of errors, semantic content, speech rate, mean length of utterance, etc. Given the large number of genres used in studies assessing aphasic narration ability (for an overview, see Müller et al., 2008), one must acknowledge the possible effects of the chosen elicitation task on the qualitative and quantitative characteristics of speech output (Armstrong, 2000), and, subsequently, the importance of evaluating verbal production across such genres (Armstrong et al., 2011). Moreover, there has been a well-established tradition of comparing data from speakers with aphasia with general corpus data, used as controls for a variety of purposes (e.g., Schwartz et al., 1994; Gahl, 2002; Fraser et al., 2015). As reference corpora become widely available for many languages, including Greek (Goutsos, 2010), there is an increasing need for developing resources with specialized data from speakers with disorders. To that end, we have developed the Greek Aphasia Error Corpus (GREAC), which is a large, searchable, web-based corpus of patients' performance on two different elicitation tasks, i.e., picture description and free narration, also including background language testing, and clinical/demographic information. The corpus is available at http://aphasia.phil.uoa.gr/, while a pilot sample of the data has been included in AphasiaBank (http://talkbank.org/AphasiaBank/). To our knowledge, this is the first publicly available corpus with data from Greek patients with aphasia. We present the first data from 50 right-handed monolingual Greek patients, with left stroke-induced aphasia, assessed at the Neuropsychology and Language Disorders Unit of the 1st Neurology Department of the National and Kapodistrian University of Athens, at Eginition Hospital. The participants (16 women) were 30–86 years old, with 4–20 years of formal schooling. Background language testing included the Boston Diagnostic Aphasia Examination–Short Form (BDAE-SF) adapted for Greek (Goodglass and Kaplan, 1983; Tsapkini et al., 2009), and the Boston Naming Test (Kaplan et al., 1983), standardized in Greek (Simos et al., 2011), CT and/or MRI scans were obtained for each patient, and two independent neuroradiologists identified lesion sites, which were then coded according to previously reported methodology (Kasselimis et al., 2017). These reports are part of the publicly available database. At this point, the structural MRIs of the patients are not included in GRAEC. Demographic and speech sample information are shown in Table 1. Informed consent for participation in the study and publication of the data (ensuring anonymity) was obtained from all participants according to the Ethics Committee of Eginition Hospital. No individually identifying information—apart from time post onset, brain lesion loci, tests' performance, and basic demographic information, including sex, age, and years of formal schooling- about the patients is contained in the corpus, and individual patients are listed by random codes (see in Supplementary Tables 1, 2, for individual information regarding lesions and BDAE scores, respectively). Table 1. Demographic and sound files information for the patients with aphasia. At present, GREAC includes 17,507 words (counting only those produced by patients) with 2,397 annotated errors. GREAC is an on-going project, aiming at a corpus of approximately 50,000 words produced by 120 patients in the following 5 years. The data included in GREAC are derived from a thorough neuropsychological assessment, during which patients were first asked to talk about their illness in the form of a semi-prompted monolog (stroke story interview) and then describe the Cookie Theft picture (Picture Description task) from the BDAE-SF (Goodglass and Kaplan, 1983). All assessments were performed by a psychologist/clinical neuropsychologist in a quiet room at the Neuropsychology and Language Disorders Unit of Eginition Hospital. The examiner first initiated a short discussion with the patient, then proceeded to medical history taking, and explained in short the process of the neuropsychological assessment. During this initial interaction, the examiner made all possible efforts to establish Rapport, and make the patient feel comfortable. After that, the speech samples were obtained. First, for the stroke story, the examiner asked the patient to describe the story of their illness: “Please tell me what happened to you when you had the stroke.” Then, the patient was asked to describe the Cookie Theft picture: “Please look carefully and describe whatever you see happening in this image.” The first task was chosen in order to elicit more natural speech data, while picture elicitation was employed to ensure more controlled discourse samples, since participants have to generate a possible story from the picture without any additional requirements on memory. It must be noted that these two genres correspond to the first two of four suggested in the AphasiaBank protocol1. These are standard tasks, widely used in the literature (see Linnik et al., 2016 for an overview) and therefore have also been employed in GREAC in order to maximize the comparability and generalizability of findings. Patients were given as much time as needed in both tasks with minimal prompting from the examiner when absolutely necessary. Furthermore, neurotypical adults performed the same tasks, with the only difference being that in the stroke story they were asked to narrate the stroke incident of another person (usually, a person with aphasia they accompany). We have already collected 50,000 words from 60 participants on these tasks, which at a later stage can be used as a reference corpus. GREAC will also include follow up data to allow for longitudinal studies investigating the nature of connected speech impairment in aphasia. The length of patients' connected speech samples ranges from 38 to 613 s. However, their actual speech is often less due to pausing and false starts. The Cookie Theft recordings range between 69 and 486 s. Stroke Story and Picture Description tasks were audio-recorded. All collected material was orthographically transcribed and checked for accuracy by a second transcriber. Transcriptions included both patients and examiners' speech; however, the examiner did not interfere in patient's narration, except from the case that patients needed to be encouraged to continue their story. Standard spelling conventions were maintained to increase consistency. However, sometimes it was necessary to deviate from standard conventions, in order to transcribe as accurately as possible what was said, like in cases of unfinished words or neologisms. Fluency problems, voiced and unvoiced starters and fillers, pauses, repetitions, and other phenomena of spoken interaction such as noise from the outside, coughing etc. were carefully noted, following conventions for spoken data transcription (Georgakopoulou and Goutsos, 2004: vii; and for Greek: Georgakopoulou and Goutsos, 1999, p. 70–72). All interjections were also transcribed to give an indication of the effortful speech of patients with aphasia. Transcribed files were named by using the patient's code and the type of interaction (f for spontaneous data, p for picture description). Preliminary findings of the corpus have been previously presented at Actas del III Congreso Internacionalde Lingüística de Corpus (Goutsos et al., 2011a). The texts included in the Corpus are kept in two different formats, plain and annotated for speech errors. The typology of errors follows the standard distinction between phonological, morphological and lexical/semantic errors found in the literature (e.g., Saling, 2007; Schwartz and Dell, 2016, cf. Schwartz et al., 1994). Following the relevant bibliography we have restricted annotation to lemma level errors, omitting e.g., pronoun referent or coherence errors (see Marini et al., 2011; Harris Wright and Capilouto, 2012) (Syntactic and other sentence level errors are included in morphosyntactic errors in order to avoid unnecessary repetition, since morphosyntactic marking is obligatory in Greek). First, participants' responses were recorded and then transcribed by transcribers trained in transcribing aphasic speech samples. During error annotation, transcribers indicated all words, phrases or sentences that they found to differ from the target word, phrase or sentence expected based on the task at hand. A second check by a different researcher was then performed in order to ascertain whether the decision was correct, excluding for instance dialectal forms or other instances of variation (e.g., learned forms used by older speakers). All discrepancies were discussed and resolved. Error classification followed, on the basis of phonological, morphological and syntactic properties of the Greek language. Error types, along with representative examples, are summarized in Table 2 (in several cases the distinction between two types of errors is impossible; in this case both types of error are annotated). Error frequencies for each patient are shown in Table 1. Further details of error annotation can be found in Goutsos et al. (2011a,b). A speech sample from the Cookie theft Picture description task, including annotations according to error types, is presented in Supplementary Table 3. Individual data on error subtypes in the present sample are provided in Supplementary Table 4. Table 2. Error categories in the GRAEC. The development of GREAC puts a much-needed emphasis on spontaneously produced data and the analysis of speech errors in their discourse context. Apart from the examination of speech errors, GREAC can be immensely helpful in the study of Greek aphasia in several other ways. First, information can be retrieved from the corpus on the frequency and types of phonological and lexical errors in Greek, including neologisms and other semantically related errors. Also, comparisons between GREAC and a reference corpus of Greek, such as the Corpus of Greek Texts (CGT, see Goutsos, 2010), or a similar corpus that contains patients' data from another language, such as the Cambridge Cookie-Theft Corpus (Williams et al., 2010), could result in interesting findings. A further interesting aspect of aphasic speech that could be explored using GREAC could be the use of combination of words or lexical bundles in terms of Biber et al. (1999). In GREAC the most frequent word combinations include phrases such as “I cannot/could not say/understand it,” “how to say it/what can I say,” “it must be,” “these things/this thing over here” (for further examples of errors, see Table 2). These findings are significant not only in revealing the discourse strategies followed by speakers with aphasia (e.g., avoidance, modality, periphrasis), but also for a further exploration of formulaic language in aphasia, which, as known, is processed in different ways than the rest of the vocabulary (e.g., Wray, 2002). More generally, extended data from aphasic discourse in languages like Greek are expected to contribute to the investigation of its linguistic properties in comparison with other languages; for example, the pilot version of GREAC has been compared to English and Hungarian data, suggesting that word frequency distribution is similar to non-aphasic discourse, whereas differences between languages can be related to languages' morphological properties and particular language impairments (Neophytou et al., 2017). The detailed error annotation can also provide important evidence for the distribution of error types, especially the pervasive phonological vs. semantic distinction (Schuchard et al., 2017; McKinnon et al., 2018; Harvey et al., 2019), as well as of sub-categories of error types, that is the relative frequency of substitution, omission, addition etc. in order to test the findings of earlier linguistic studies of aphasic discourse (e.g., Blumstein, 1973; Lesser, 1995). More details can be obtained for e.g., the distribution of phonetic vs. phonemic errors (Ash et al., 2010), semantic errors vs. errors of omission (Bormann et al., 2008), the characteristics of errors of omission (vs. errors of commission, Chen et al., 2019), the target relatedness of neologistic errors (Pilkington et al., 2017) etc. Moreover, individual information on speech sample characteristics, such as total number of words and duration, could be used by researchers for participant selection according to specific exclusion criteria, or as covariates in statistical analyses. Finally, by relating data to metadata, including the level of severity of aphasia, GREAC can contribute to the development of a baseline for Greek for the automatic recognition of aphasic speech (cf. Le and Mower Prevost, 2016 for English). Furthermore, the question of aphasia types can be studied on a much firmer basis. Different speech errors have been associated in the literature with different aphasia types (Goodglass, 1981). For example, errors in tense and agreement marking have been associated with non-fluent types of aphasia (e.g., Friedmann and Grodzinsky, 1997), whereas phonological errors and neologisms have been associated with fluent types of aphasia (e.g., Schwartz et al., 2004; Stenneken et al., 2008). However, group studies have shown that patients belonging to different diagnostic categories often made similar errors (e.g., Ardila and Rosselli, 1993). By keeping a separate file on metadata such as the demographic and clinical characteristics of patients, we would be able to link language problems with the clinical assessment of aphasic deficits. Thus, it would be possible to revisit the criteria of distinguishing between phenotypes of aphasia on the basis of findings from linguistic errors, instead of following the traditional taxonomy; in this sense, openly shared databases like GREAC could aid in the effort to cut the traditional aphasia classification cord, and move forward toward more progressive schemas (see also Schwartz, 1984; Caplan, 1993; Basso, 2000; Charidimou et al., 2014; Tremblay and Dick, 2016; Kasselimis et al., 2017). Finally, follow-up data would allow for longitudinal studies on the nature of connected speech impairment in different types of aphasia. Two issues remain to be addressed. The first one is the justification of the existence of GREAC as a standalone database. There are several reasons that led us to the decision to create GREAC. First, the number of participants is much greater compared to the Greek sample included in the AphasiaBank for instance. Second, the addition of metadata is important; as stated above, apart from demographics, GRAEC includes individual scores on BDAE, as well as lesion information. The inclusion of such variables in statistical analyses could strengthen the findings of any aphasiological study that would utilize our database. Third, as data collection progresses, we will be able to add data from more patients, as well as data from follow-up assessments from patients already included in the corpus. Our Unit is mainly focusing on language disorders, and therefore several patients with aphasia are referred to us by other Units inside Eginition Hospital, but also by other collaborating clinics. Moreover, we regularly perform follow-up assessments for clinical and research purposes, i.e., monitor the course of aphasic deficits for individual patients or investigate the recovery pattern and possible predictors of recovery at the group level (see for example, our small scale study conducted a few years ago, which included data from the acute and the chronic phase: Chatziantoniou et al., 2015). Such follow-up data have already been collected, and will gradually be incorporated in GRAEC. The second issue is that of sample size. There have been several databanks published in other disciplines, usually in the framework of large epidemiological studies, which include tens or even hundreds of thousands of participants. However, the GREAC is not an epidemiological databank. Its purpose is to make speech data from Greek patients with aphasia available to any researcher who wants to study aphasic errors in Greek language. To the best of our knowledge, aphasiological studies (usually in the field of psycho- or neuro-linguistics) presenting rather interesting results on Greek aphasia have samples that do not exceed the number of 20 participants (e.g., Stavrakaki and Kouvava, 2003; Koukoulioti and Stavrakaki, 2014). We argue that similar studies in the future would have much more robust and generalizable results by using a greater sample derived from GRAEC. Moreover, the fact that interested researchers would have the opportunity to select samples with specific characteristics on the basis of the metadata included in GRAEC, could lead to more focused studies. Considering how difficult patient recruiting is, let alone sampling that results in a homogenous group of participants, we believe that the present databank will aid researchers to save time and allocate their resources to aspects other than baseline testing, identifying patients suitable for their study, and speech data collecting. To summarize, the GREAC is a unique data source for Greek that provides a rich resource for future research in many aspects of language deficits in aphasia. It allows for studying large amounts of naturally occurring data, by focusing on actual language use. The data included in GREAC come from conditions which are closer to conversation or natural discourse than experimental elicitation data, based on comprehension and production tests. Therefore, although they are not of the same ecological validity as data derived from natural verbal interaction, they can help us identify phenomena that could not have occurred if a more traditional experimental design was followed. It also allows for assessing “the relative probability of particular symptom patterns and their possible etiology” (Bates et al., 1987, p. 25) and statistically evaluating aspects of actual language usage (e.g., Wright et al., 2003). Thus, we can both generalize across patients' linguistic symptoms, by treating their discourse as a coherent whole, and study individual variation by setting it against the general pattern. The datasets generated for this study are available on request to the corresponding author. GREAC is available at http://aphasia.phil.uoa.gr/. The studies involving human participants were reviewed and approved by Eginition Hospital Ethics Committee, National and Kapodistrian Athens, School of Medicine, Greece. The patients/participants provided their written informed consent to participate in this study. DK contributed to the conceptualization and design of the study, performed clinical language testing, and wrote the manuscript. MV contributed to the conceptualization and design of the study, performed linguistic data processing, and wrote the manuscript. GA performed linguistic data processing, and revised the manuscript. IE contributed to the design of the study and revised the manuscript. DG conceived and designed the study, supervised linguistic data processing, and wrote the manuscript. CP contributed to the conceptualization and design of the study, recruited patients, supervised clinical language testing, and revised the manuscript. All authors contributed to the article and approved the submitted version. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors would like to thank the patients who participated in this study. We also acknowledge the financial contribution of the Dean of the School of Philosophy, through the Special Account for Research Grants of the University of Athens. DK was supported by IKY Foundation co-financed by ESF and Greek national funds through action MIS5033021 of the Operational Programme Human Resources Development Program, Education and Lifelong Learning of the NSRF 2014–2020. The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01577/full#supplementary-material 1. ^Retrieved at http://aphasia.talkbank.org/protocol/ Ardila, A., and Rosselli, M. (1993). Language deviations in aphasia: a frequency analysis. Brain Lang. 44, 165–180. doi: 10.1006/brln.1993.1011 PubMed Abstract | CrossRef Full Text | Google Scholar Armstrong, E. (2000). Aphasic discourse analysis: the story so far. Aphasiology 14, 875–892. doi: 10.1080/02687030050127685 CrossRef Full Text | Google Scholar Armstrong, E., Ciccone, N., Godecke, E., and Kok, B. (2011). Monologues and dialogues in aphasia: some initial comparisons. Aphasiology 25, 1347–1371. doi: 10.1080/02687038.2011.577204 CrossRef Full Text | Google Scholar Ash, S., McMillan, C., Gunawardena, D., Avants, B., Morgan, B., Khan, A., et al. (2010). Speech errors in progressive non-fluent aphasia. Brain Lang. 113, 13–20. doi: 10.1016/j.bandl.2009.12.001 PubMed Abstract | CrossRef Full Text | Google Scholar Avrutin, S. (2001). Linguistics and agrammatism. Glot Int. 5, 1–11. Google Scholar Badecker, W., and Caramazza, A. (1985). On considerations of method and theory governing the use of clinical categories in neurolinguistics and cognitive neuropsychology: the case against agrammatism. Cognition 20, 97–125. doi: 10.1016/0010-0277(85)90049-6 PubMed Abstract | CrossRef Full Text | Google Scholar Basso, A. (2000). The aphasias: fall and renaissance of the neurological model? Brain Lang. 71, 15–17. doi: 10.1006/brln.1999.2199 PubMed Abstract | CrossRef Full Text | Google Scholar Bates, E., Friederici, A., and Wulfeck, B. (1987). Comprehension in aphasia: a cross-linguistic study. Brain Lang. 32, 19–67. doi: 10.1016/0093-934X(87)90116-7 PubMed Abstract | CrossRef Full Text | Google Scholar Biber, D., Johansson, S., Leech, G., Conrad, S., and Finegan, E. (1999). Longman Grammar of Spoken and Written English. London: Longman. Google Scholar Blumstein, S. E. (1973). A Phonological Investigation of Aphasic Speech. The Hague: Mouton. doi: 10.1515/9783110887433 CrossRef Full Text | Google Scholar Bormann, T., Kulke, F., Wallesch, C.-W., and Blanken, G. (2008). Omissions and semantic errors in aphasic naming: is there a link? Brain Lang. 104, 24–32. doi: 10.1016/j.bandl.2007.02.004 PubMed Abstract | CrossRef Full Text | Google Scholar Caplan, D. (1993). Toward a psycholinguistic approach to acquired neurogenic language disorders. Am. J. Speech Lang. Pathol. 2, 59–83. doi: 10.1044/1058-0360.0201.59 CrossRef Full Text | Google Scholar Caramazza, A. (1986). On drawing inferences about the structure of normal cognitive processes from patterns of impaired performance: the case for single patient studies. Brain Cognit. 5, 41–66. doi: 10.1016/0278-2626(86)90061-8 PubMed Abstract | CrossRef Full Text | Google Scholar Caramazza, A., and Badecker, W. (1991). Clinical syndromes are not God's gift to cognitive neuropsychology: a reply to a rebuttal to an answer to a response to the case against syndrome-based research. Brain Cognit. 16, 211–227. doi: 10.1016/0278-2626(91)90007-U PubMed Abstract | CrossRef Full Text | Google Scholar Charidimou, A., Kasselimis, D., Varkanitsa, M., Selai, C., Potagas, C., and Evdokimidis, I. (2014). Why is it difficult to predict language impairment and outcome in patients with aphasia after stroke? J. Clin. Neurol. 10, 75–83. doi: 10.3988/jcn.2014.10.2.75 PubMed Abstract | CrossRef Full Text | Google Scholar Chatziantoniou, L., Kasselimis, D., Kyrozis, A., Ghika, A., Kourtidou, P., Peppas, C., et al. (2015). Lesion size and initial severity as predictors of aphasia outcome. Stem Spraak en Taalpathologie 20, 34–35. Google Scholar Chen, Q., Middleton, E., and Mirman, D. (2019). Words fail: Lesion-symptom mapping of errors of omission in post-stroke aphasia. J. Neuropsychol. 13, 183–197. doi: 10.1111/jnp.12148 PubMed Abstract | CrossRef Full Text | Google Scholar Fraser, K. C., Ben-David, N., Hirst, G., Graham, N. L., and Rochon, E. (2015). “Sentence segmentation of aphasic speech.” in Human Language Technologies: The 2015 Annual Conference of the North American Chapter of the ACL (Denver, CO: Association for Computational Linguistics), 862–871. doi: 10.3115/v1/N15-1087 CrossRef Full Text | Google Scholar Friedmann, N., and Grodzinsky, Y. (1997). Tense and agreement in agrammatic production: prunning the syntactic tree. Brain Lang. 56, 397–425. doi: 10.1006/brln.1997.1795 CrossRef Full Text | Google Scholar Gahl, S. (2002). Lexical biases in aphasic sentence comprehension: an experimental and corpus linguistic study. Aphasiology 16, 1173–1198. doi: 10.1080/02687030244000428 CrossRef Full Text | Google Scholar Georgakopoulou, A., and Goutsos, D. (1999). Text and Communication [In Greek]. Athens: Ellinika Grammata. Georgakopoulou, A., and Goutsos, D. (2004). Discourse Analysis. An Introduction, 2nd Edn. Edinburgh: Edinburgh University Press. doi: 10.3366/edinburgh/9780748620456.001.0001 CrossRef Full Text | Google Scholar Goodglass, H. (1981). The syndromes of aphasia: similarities and differences in neurolinguistic features. Top. Lang. Disord. 1, 1–14. doi: 10.1097/00011363-198109000-00004 CrossRef Full Text | Google Scholar Goodglass, H., and Kaplan, E. (1983). The Assessment of Aphasia and Related Disorders, 2nd Edn. Philadelphia, PA: Lea and Febiger. Goutsos, D. (2010). The corpus of Greek texts: a reference corpus for Modern Greek. Corpora 5, 29–44. doi: 10.3366/cor.2010.0002 CrossRef Full Text | Google Scholar Goutsos, D., Potagas, C., Kasselimis, D., Varkanitsa, M., and Evdokimidis, I. (2011a). “The corpus of Greek aphasic speech: design and compilation,” in Las tecnologías de la información y las comunicaciones: Presente y futuro en el análisis de córpora. Actas del III Congreso Internacional de Lingüística de Corpus. Valencia: Universitat Politècnica de València, eds M. L. Carrió Pastor and M. A. Candel Mora (Valencia: Universitat Politècnica de València, 77–86. Google Scholar Goutsos, D., Potagas, C., Kasselimis, D., Varkanitsa, M., and Evdokimidis, I. (2011b). “Studying paraphasias in a corpus of Greek aphasic discourse [In Greek],” in Language and Memory, eds C. Potagas and I. Evdokimidis (Athens: Synapses, 23–47. Grodzinsky, Y., Piñango, M., Zurif, E., and Drai, D. (1999). The critical role of group studies in neuropsychology: comprehension regularities in Broca's aphasia. Brain Lang. 67, 134–147. doi: 10.1006/brln.1999.2050 PubMed Abstract | CrossRef Full Text | Google Scholar Harris Wright, H., and Capilouto, G. J. (2012). Considering a multi-level approach to understanding maintenance of global coherence in adults with aphasia. Aphasiology 26, 656–672. doi: 10.1080/02687038.2012.676855 PubMed Abstract | CrossRef Full Text | Google Scholar Harvey, D. Y., Massa, J. A., Shah-Basaka, P., Wurzman, R., Faseyitana, O., Sacchettia, D. L., et al. (2019). Continuous theta burst stimulation over right pars triangularis facilitates naming abilities in chronic post-stroke aphasia by enhancing phonological access. Brain Lang. 192, 25–34. doi: 10.1016/j.bandl.2019.02.005 PubMed Abstract | CrossRef Full Text | Google Scholar Kaplan, E., Goodglass, H., and Weintraub, S. (1983). Boston Naming Test. Philadelphia, PA: Lea and Febiger. Kasselimis, D. S., Simos, P. G., Peppas, C., Evdokimidis, I., and Potagas, C. (2017). The unbridged gap between clinical diagnosis and contemporary research on aphasia: a short discussion on the validity and clinical utility of taxonomic categories. Brain Lang. 164, 63–67. doi: 10.1016/j.bandl.2016.10.005 PubMed Abstract | CrossRef Full Text | Google Scholar Khudyakova, M., Bergelson, M., Akinina, Y., Iskra, E., Toldova, S., and Dragoy, O. (2016). “Russian CliPS: a corpus of narratives by brain-damaged individuals,” in Proceedings of LREC 2016 Workshop. Resources and Processing of Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric Impairments (RaPID-2016) (Linköping: Linköping University Electronic Press). Google Scholar Kong, A. P. H., and Law, S. P. (2019). Cantonese AphasiaBank: an annotated database of spoken discourse and co-verbal gestures by healthy and language-impaired native Cantonese speakers. Behav. Res. Methods 51, 1131–1144. doi: 10.3758/s13428-018-1043-6 PubMed Abstract | CrossRef Full Text | Google Scholar Koukoulioti, V., and Stavrakaki, S. (2014). Producing and inflecting verbs with different argument structure: evidence from Greek aphasic speakers. Aphasiology 28, 1320–1349. doi: 10.1080/02687038.2014.919561 CrossRef Full Text | Google Scholar Kuvač Kraljević, J., HrŽica, G., and Lice, K. (2017). CroDA: a Croatian discourse corpus of speakers with aphasia. Hrvatska revija za rehabilitacijska istrazivanja 53, 61–71. doi: 10.31299/hrri.53.2.5 CrossRef Full Text | Google Scholar Laures-Gore, J., Russell, S., Patel, R., and Frankel, M. (2016). The Atlanta motor speech disorders corpus: motivation, development, and utility. Folia Phoniatrica Logopaedica 68, 99–105. doi: 10.1159/000448891 PubMed Abstract | CrossRef Full Text | Google Scholar Le, D., and Mower Prevost, E. (2016). Improving automatic recognition of aphasic speech with AphasiaBank. Interspeech 2681–2685. doi: 10.21437/Interspeech.2016-213 CrossRef Full Text | Google Scholar Lesser, R. (1995). Linguistic Investigations of Aphasia. London: Whurr. Linnik, A., Bastiaanse, R., and Höhle, B. (2016). Discourse production in aphasia: a current review of theoretical and methodological challenges. Aphasiology 30, 765–800. doi: 10.1080/02687038.2015.1113489 CrossRef Full Text | Google Scholar MacWhinney, B., Fromm, D., Forbes, M., and Holland, A. (2011). AphasiaBank: methods for studying discourse. Aphasiology 25, 1286–1307. doi: 10.1080/02687038.2011.589893 PubMed Abstract | CrossRef Full Text | Google Scholar Marini, A., Andreetta, S, del Tin, S., and Carlomagno, S. (2011). A multi-level approach to the analysis of narrative language in aphasia. Aphasiology 25, 1372–1392. doi: 10.1080/02687038.2011.584690 CrossRef Full Text | Google Scholar McEnery, T., and Hardie, A. (2012). Corpus Linguistics: Method, Theory and Practice. Cambridge: Cambridge University Press. doi: 10.1017/CBO9780511981395 CrossRef Full Text | Google Scholar McKinnon, E. T., Fridriksson, J., Basilakos, A., Hickok, G., Hillis, A. E., Spampinato, M. V., et al. (2018). Types of naming errors in chronic post-stroke aphasia are dissociated by dual stream axonal loss. Nat. Sci. Rep. 8:14352. doi: 10.1038/s41598-018-32457-4 PubMed Abstract | CrossRef Full Text | Google Scholar Mirman, D., Strauss, T. J., Brecher, A., Walker, G. M., Sobel, P., Dell, G. S., et al. (2010). A large, searchable, web-based database of aphasic performance on picture naming and other tests of cognitive function. Cognit. Neuropsychol. 27, 495–504. doi: 10.1080/02643294.2011.574112 PubMed Abstract | CrossRef Full Text | Google Scholar Müller, N., Guendouzi, J. A., and Wilson, B. (2008). Discourse analysis and communication impairment. In: The handbook of Clinical Linguistics. eds M. J. Ball, M. R. Perkins, N. Müller, and S. Howard (Blackwell Oxford), 3–31. doi: 10.1002/9781444301007.ch1 PubMed Abstract | CrossRef Full Text | Google Scholar Neophytou, K., van Egmond, M., and Avrutin, S. (2017). Zipf's law in aphasia across languages: a comparison of English, Hungarian and Greek. J. Quant. Linguist. 24, 178–196. doi: 10.1080/09296174.2016.1263786 CrossRef Full Text | Google Scholar Pilkington, E., Keidel, J., Kendrick, L. T., Saddy, J. D., Sage, K., and Robson, H. (2017). Repetition: perseverative, neologistic, and lesion patterns in jargon aphasia. Front. Hum. Neurosci. 11:225. doi: 10.3389/fnhum.2017.00225 PubMed Abstract | CrossRef Full Text | Google Scholar Saling, M. M. (2007). “Disorders of language,” in Neurology and Clinical Neuroscience, ed A. H. V. Schapira (Amsterdam: Elsevier), 31–42. doi: 10.1016/B978-0-323-03354-1.50007-9 CrossRef Full Text | Google Scholar Schuchard, J., Middleton, E. L., and Schwartz, M. F. (2017). The timing of spontaneous detection and repair of naming errors in aphasia. Cortex 93, 79–91. doi: 10.1016/j.cortex.2017.05.008 PubMed Abstract | CrossRef Full Text | Google Scholar Schwartz, M. F. (1984). What the classical aphasia categories can't do for us, and why. Brain Lang. 21, 3–8. doi: 10.1016/0093-934X(84)90031-2 PubMed Abstract | CrossRef Full Text | Google Scholar Schwartz, M. F., and Dell, G. S. (2016). “Word production from the perspective of speech errors in aphasia,” in Neurobiology of Language, eds G. Hickok and S. L. Small (Amsterdam: Elsevier), 701–715. doi: 10.1016/B978-0-12-407794-2.00056-0 CrossRef Full Text | Google Scholar Schwartz, M. F., Saffran, E. M., Blocch, D. E., and Dell, G. S. (1994). Disordered speech production in aphasic and normal speakers. Brain Lang. 47, 52–88. doi: 10.1006/brln.1994.1042 PubMed Abstract | CrossRef Full Text | Google Scholar Schwartz, M. F., Wilshire, C. E., Gagnon, D. A., and Polansky, M. (2004). Origins of nonword phonological errors in aphasic picture naming. Cognit. Neuropsychol. 21, 159–186. doi: 10.1080/02643290342000519 PubMed Abstract | CrossRef Full Text | Google Scholar Simos, P. G., Kasselimis, D., and Mouzaki, A. (2011). Age, gender, and education effects on vocabulary measures in Greek. Aphasiology 25, 475–491. doi: 10.1080/02687038.2010.512118 CrossRef Full Text | Google Scholar Stavrakaki, S., and Kouvava, S. (2003). Functional categories in agrammatism: evidence from Greek. Brain Lang. 86, 129–141. doi: 10.1016/S0093-934X(02)00541-2 PubMed Abstract | CrossRef Full Text | Google Scholar Stenneken, P., Hofmann, M. J., and Jacobs, A. M. (2008). Sublexical units in aphasic jargon and in the standard language: comparative analyses of neologisms in connected speech. Aphasiology 22, 1142–1156. doi: 10.1080/02687030701820501 CrossRef Full Text | Google Scholar Tremblay, P., and Dick, A. S. (2016). Broca and Wernicke are dead, or moving past the classic model of language neurobiology. Brain Lang. 162, 60–71. doi: 10.1016/j.bandl.2016.08.004 PubMed Abstract | CrossRef Full Text | Google Scholar Tsapkini, K., Vlahou, C. H., and Potagas, C. (2009). Adaptation and validation of standardized aphasia tests in different languages: lessons from the Boston Diagnostic Aphasia Examination. Behav. Neurol. 22, 111–119. doi: 10.1155/2010/423841 PubMed Abstract | CrossRef Full Text | Google Scholar Varlokosta, S., Stamouli, S., Karasimos, A., Markopoulos, G., Kakavoulia, M., Nerantzini, M., et al. (2016). “A Greek corpus of aphasic discourse: collection, transcription, and annotation specifications,” in Proceedings of LREC 2016 Workshop. Resources and Processing of Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric Impairments (RaPID-2016), Monday 23rd of May 2016 (No. 128) (Linköping: Linköping University Electronic Press). Google Scholar Westerhout, E., and Monachesi, P. (2007). A Pilot Study for a Corpus of Dutch Aphasic Speech (CoDAS). Available online at: http://citeseerx.ist.psu.edu/viewdoc/download? Google Scholar Williams, C., Thwaites, A., Buttery, P., Geertzen, J., Randall, B., Shafto, M., et al. (2010). “The Cambridge Cookie-Theft Corpus: a corpus of directed and spontaneous speech of brain-damaged patients and healthy individuals,” in Proceedings of the International Conference on Language Resources and Evaluation (Valletta: European Language Resources Association (ELRA)). Google Scholar Wray, A. (2002). Formulaic Language and the Lexicon. Cambridge: Cambridge University Press. doi: 10.1017/CBO9780511519772 CrossRef Full Text | Google Scholar Wright, H. H., Silverman, S., and Newhoff, M. (2003). Measures of lexical diversity in aphasia. Aphasiology 17, 443–452. doi: 10.1080/02687030344000166 PubMed Abstract | CrossRef Full Text | Google Scholar Keywords: Greek, corpora, aphasia, errors, discourse, narration Citation: Kasselimis D, Varkanitsa M, Angelopoulou G, Evdokimidis I, Goutsos D and Potagas C (2020) Word Error Analysis in Aphasia: Introducing the Greek Aphasia Error Corpus (GRAEC). Front. Psychol. 11:1577. doi: 10.3389/fpsyg.2020.01577 Received: 06 February 2020; Accepted: 12 June 2020; Published: 04 August 2020. Edited by: Reviewed by: Copyright © 2020 Kasselimis, Varkanitsa, Angelopoulou, Evdokimidis, Goutsos and Potagas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Dimitrios Kasselimis, [email protected] These authors have contributed equally to this work
Stefano Gelibter, Lucia Moiola, Stefano Carlo Previtali,
Published: 9 July 2020
Journal of Neurology, Volume 267, pp 2744-2746; https://doi.org/10.1007/s00415-020-10049-5

The publisher has not yet granted permission to display this abstract.
, Barbara Stawinska-Witoszynska
Published: 7 July 2020
Frontiers in Neurology, Volume 11; https://doi.org/10.3389/fneur.2020.00571

Abstract:
Gender plays a role in the prevalence and natural course of several disorders. It is apparent in neurodegenerative diseases like Parkinson's disease more prevalent in men, Alzheimer's disease more prevalent in women, or Lewy body dementia more prevalent in men. Rarely, however, recently more often, in autosomally conditioned diseases, gender differences are being identified (1–21). Huntington's disease (HD) as a rare neurodegenerative (recently reported peripheral tissue involvement), incurable—therefore still displaying natural course—disorder with an autosomal dominant pattern of inheritance with full penetrance in most cases (22). Therefore it was not explored for gender differences for many years. Gender was considered in HD, however, with respect to a parent of an HD patient, as it was observed that disease inherited from a father resulted in symptoms anticipation, namely earlier onset and faster progression than when inherited from the mother (23–25). It was later explained by a higher probability for elongation of the causative HD mutation during spermatogenesis, than during oogenesis (26, 27). Very quickly after identification of a causative mutation in 1993, it was observed that there is a negative correlation between the number of CAG repeats in the causative gene (expansion of causative mutation) and onset age, namely larger expansion of CAG repetitive sequence in the HD gene resulted in earlier HD symptoms onset (28). The rate of HD progression was explored early after the causative gene identification. Interesting results were observed in small groups (29–31), but lack of proper tools for progression measurement resulted in a lack of any results indicating a correlation between CAG repeats expansion and rate of the disease progression (32–35). First important (36) and deciding findings confirming that the rate of HD progression is dependent on CAG repeats number were described in 2008 in a large HD cohort study (37). In the same year, another small study indicated gender differences in HD patients (38). The finding described in the research paper published in 2008 (38) came rather as a surprise during the study's data analysis (38). First, the study was based on just 41 HD patients: 24 women (38). A statistically significant correlation between the number of CAG repeats and scores in Unified Huntington's Rating Scale (UHDRS) subscales—motor, cognitive, functional, independence and total functional capacity (TFC)—were identified in women but not in men. Moreover, time from onset correlated with scores in above listed UHDRS subscales in women only. These findings provided insight allowing further investigation to study gender differences in HD. In 2013, a large cohort analysis based on 1,267 HD affected individuals was performed (39). This study based on data collected in REGISTRY, an Observational Study of the European Huntington's Disease Network (EHDN) (40) population, and was aimed to identify gender differences in several HD features including differences in the rate of the disease progression based on following annual visits when patients were assessed in UHDRS subscales. The study was controlled for several environmental factors. The most important finding of this study was identification of a significant gender difference in the rate of HD progression controlled among other things for disease burden (calculated variable incorporating CAG repeats number in larger allele and age): DB = (CAG number in the larger allele – 35.5) × age in years (41). Disease burden reflects the stage of brain pathology in HD and includes the factor responsible for 70% of the variability in HD onset, namely CAG repeats number in mutated HTT allele (42, 43). Both genders did not differ for the disease burden or for onset age, but the progression rate in women was faster. Other gender differences were identified in cross-sectional analysis for years of education (men studied longer), presence of depression (in women more often), history of depression (in women more often), alcohol abuse (more often in men), and cigarette smoking (more often in men). To explain the observed difference in the rate of HD progression, the longitudinal analysis was controlled for several variables like disease duration, years of education, presence of depression, depressive episodes in the past, history of psychotic disturbances, history of obsessive-compulsive disorders, history of suicidal ideation, smoking, alcohol abuse, and drug usage. These variables were used as confounders in the longitudinal analysis did not clarify the inter-gender differences in progression rate. The above-described study provided solid evidence to confirm the presence of gender differences in HD clinical picture and what is more important in rate of HD progression. Above mentioned findings were confirmed and explored in more detail in 2018 in another study, which aimed at a gender effect in particular symptomatic domains of HD and their contribution to functional abilities and quality of life (44). The clinical picture of HD is formed by three major symptomatic domains, namely motor symptoms (e.g., clumsiness, chorea, dystonia), cognitive impairment (subcortical dementia), and behavioral disturbances (e.g., depression, apathy, irritability, and aggression) (22). All of these domains contribute to functional abilities of affected individuals but the impact of a particular domain for progression of functional disability was not clearly explained in previously published studies (45–48). Considering the study (39), it was important to evaluate also gender role in this contribution. The study (44) based on 2,191 HD affected individuals (1,080 women), REGISTRY study's participants, annually examined in UHDRS for several years, was controlled for the same factors as previously described study. In women significantly stronger correlations between all symptomatic spheres and HD progression rate was observed, motor domain contributed the most, followed by cognitive and behavioral; moreover, motor symptoms were responsible for more variability in functional abilities in women than in men, while cognitive symptoms had an opposite contribution (more variability in men than in women). This means that motor symptoms were the strongest contributors to functional abilities in both genders, particularly in women, although cognitive symptoms are more important in men for functional abilities than in women. One year after the previously described research paper was published, another cohort study delivered surprising results (49). Here it was observed in a huge cohort of 67 millions of Americans performed between 2003 and 2016 that HD has a significantly higher prevalence in women estimated on 7.05 per 100,000 than in men, 6.91 per 100,000. This result may suggest a more severe HD pathologic process in women. There is also evidence based on pre-clinical settings where in animal HD models gender differences were identified. The first important research paper was published in 2007; in the murine model, it was observed that a lower level of extracellular ascorbate in the striatum in males reflected a more severe phenotype than in females (50). In 2008 another study suggested the neuroprotective effect of 17b-estradiol in females in rat HD model (51). In 2016 in BACHD mouse model of Huntington's disease, it was found that circadian activity levels, rhythm precision, and behavioral fragmentation are more severe in males (52). Finally, in 2019 more severe deficits in neuroprotective nitric oxide synthase activity in the HD cortex and striatum were observed mostly in Q175 males of HD mouse model (53). In contrast to human studies, animal research results indicate a more severe picture in males. Above mentioned studies indicate presence of gender differences in HD. The limitation factor of the finding (39, 44) is a lack of inclusion of two important, however difficult to be assessed variables, namely concomitant disorders and medications. There is still a question whether differences in these two variables could explain gender differences reported so far (39, 44). Depression was considered as a confounder in both studies but did not explain differences in HD progression rate between genders. The prevalence of non-psychiatric concomitant disorders does not differ between genders as reported in recently published paper (54). It seems that both genders are not treated differently in HD but this should be explored in detail. Also animal studies, confirming gender differences, bring contrary results of gender burden, displaying their ample limitations in explanation of pathologic processes behind observed differences (50–53). The findings in animal models suggest their imperfection rather (55). Hormonal disturbances observed in an animal model (51) and in HD patients (56, 57) could be supportive to explain the phenomenon, but a cause of the gender differences in HD seems to be more complex and require future studies in larger HD cohorts. A recently published study on huntingtin's or HTT gene role in neurodevelopment in boys and girls (58) showed that in girls a longer CAG sequence in larger allele (still in normal range) correlated with thicker cortex and better cognition when in boys this impact was weaker being restricted mainly to lower putamen/cerebellum volume ratio in boys with higher CAG repeats number (58). This observation could suggest that also HTT gene mutation in women could exert stronger impact than in men. The gender differences were identified in other neurodegenerative movement disorders caused by a dynamic mutation (CAG repeats expansion, similar to this causing HD, so called because number of CAG repeats may change during meiosis, or during mitosis) located in autosomal genes, namely in spinocerebellar ataxia (SCA) type 3 and 6 (13). In women, the progression of non-ataxia motor symptoms was faster than in men in those diseases. This effect in SCA type 3 was confirmed in a follow up study on the same European cohort (14). Moreover, faster progression in women with SCA 2 and 3 was reported in other studies (15, 16). This is consistent with findings in HD, suggesting that gender differences could be related to specific dynamic mutation mechanism, making it different than other monogenetic disorders. Moreover, gender differences were described in various CACNA1A gene mutations, e.g., SCA 6 as an example of trinucleotides extension, episodic ataxia type 2 (EA2) in case of loss-of-function, and familial hemiplegic migraine-1 (FHM1) an example of gain-of-function missense mutation. In women, EA2 and FHM1 phenotypes were present when in men with the same mutation not (17). Interestingly also in CACNA1S gene mutation, which is another channel disorder, namely hypokalemic periodic paralysis, gender differences based on reduced penetrance in women and full in men were identified (18). Gender differences in monogenetic autosomal neurological diseases do not always result in worsened progression or more severe clinical picture in one gender. This was confirmed by observation in Neurofibromatosis Type 1 (NF1). In a study conducted across girls and boys using “MOXO test,” which is being used for patients with ADHD, it was found that while the boys performed better than girls in attention and timing, they exhibited worse scores for impulsivity and hyperactivity. The observed difference does not comply with findings in general ADHD population, therefore a contribution of NF1 gene mutation is likely (12). Gender differences in monogenetic neurologic disorders can be also race dependent as in the case of facioscapulohumeral muscular dystrophy type 1 (FSHD). The mutation in this disease similarly to this in HD is located in chromosome 4 and is related to nucleic acid length but in contrast refers to another place on the chromosome and results not from extension but from the contraction of D4Z4 repeats number. In a Korean study, it was identified that women are more seriously affected than men (1) when in studies based on European cohorts, men were more seriously affected (2–5). It was controlled for age and D4Z4 repeats number (1). In another neuromuscular monogenetic disease, namely Charcot–Marie–Tooth type 1A, women present a more severe phenotype and earlier onset age (6–9). It seems that gender differences, apart from those observed in SCA, in monogenetic neurological diseases do not reflect differences described in HD. In monogenetic non-neurologic diseases, the more severe picture was described in men with autosomal dominant polycystic kidney disease (19, 20). In thalassemia major bone mass reduction was more prevalent and more severe in men. This finding was however accompanied by another one that women were more vulnerable for bones mass loss when hypogonadism co-existed, therefore, hormonal contribution seems to play an important role in observed differences (21). Hormonal factors partially explain more prevalent clinical manifestation of acute intermittent porphyria in women, in which the acute attacks occur rarely before puberty and its frequency and severity decline after menopause (10). Lower level of estrogens in postmenopausal women has been suggested to explain also more severe atherosclerosis in women affected by familial hypercholesterolemia (11). Sex differences in common movement disorders were nicely summarized in a review published earlier this year (59). In movement's disorders, diseases' severity fluctuations related to menstrual cycle in women were reported in female patients with Parkinson's disease (60–62) and dystonia (63). It shed light on the effect of estrogens, indicating potential worsening in post-menopausal women (64). Future research in HD should, therefore, consider clinical differences between pre- and post-menopausal women included these on HRT to elucidate this effect in HD. Currently, it is clear that there are gender differences in non-sex-linked genetic disorders. They are not well-understood; therefore, they ought to be investigated further, as they could shed light on disease mechanisms and pathogenesis. In diseases where gender differences were identified, the modeling, design, and interpretation of observational studies and clinical trials should be performed with respect to the gender of participants. All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Assistant Professor Michal Mielcarek for language editing. 1. Park HJ, Hong JM, Lee JH, Lee HS, Shin HY, Kim SM, et al. Low D4Z4 copy number and gender difference in Korean patients with facioscapulohumeral muscular dystrophy type 1. Neuromuscul Disord. (2015) 25:859–64. doi: 10.1016/j.nmd.2015.08.004 PubMed Abstract | CrossRef Full Text | Google Scholar 2. Ricci G, Scionti I, Sera F, Govi M, D'Amico R, Frambolli I, et al. Large scale genotype-phenotypeanalyses indicate that novel prognostic tools are required for families with facioscapulohumeral muscular dystrophy. Brain. (2013) 136:3408–17. doi: 10.1093/brain/awt226 PubMed Abstract | CrossRef Full Text | Google Scholar 3. Sakellariou P, Kekou K, Fryssira H, Sofocleous C, Manta P, Panousopoulou A, et al. Mutation spectrum and phenotypic manifestation in FSHD Greek patients. Neuromuscul Disord. (2012) 22:339–49. doi: 10.1016/j.nmd.2011.11.001 PubMed Abstract | CrossRef Full Text | Google Scholar 4. Tonini MM, Passos-Bueno MR, Cerqueira A, Matioli SR, Pavanello R, Zatz M. Asymptomatic carriers and gender differences in facioscapulohumeral muscular dystrophy (FSHD). Neuromuscul Disord. (2004) 14:33–8. doi: 10.1016/j.nmd.2003.07.001 PubMed Abstract | CrossRef Full Text | Google Scholar 5. Zatz M, Marie SK, Cerqueira A, Vainzof M, Pavanello RC, Passos-Bueno MR. The facioscapulohumeral muscular dystrophy (FSHD1) gene affects males more severely and more frequently than females. Am J Med Genet. (1998) 77:155–61. doi: 10.1002/(SICI)1096-8628(19980501)77:23.0.CO;2-R PubMed Abstract | CrossRef Full Text | Google Scholar 6. Padua L, Aprile I, Cavallaro T, Commodari I, La Torre G, Pareyson D, et al. Variables influencing quality of life and disability in Charcot Marie Tooth (CMT) patients: Italian multicentre study. Neurol Sci. (2006) 27:417–23. doi: 10.1007/s10072-006-0722-8 PubMed Abstract | CrossRef Full Text | Google Scholar 7. Padua L, Shy ME, Aprile I, Cavallaro T, Pareyson D, Quattrone A, et al. Correlation between clinical/neurophysiological findings and quality of life in Charcot-Marie-Tooth type 1A. J Peripher Nerv Syst. (2008) 13:64–70. doi: 10.1111/j.1529-8027.2008.00159.x PubMed Abstract | CrossRef Full Text | Google Scholar 8. Vinci P, Serrao M, Millul A, Deidda A, De Santis F, Capici S, et al. Quality of life in patients with Charcot-Marie-Tooth disease. Neurology. (2005) 65:922–4. doi: 10.1212/01.wnl.0000176062.44360.49 PubMed Abstract | CrossRef Full Text | Google Scholar 9. Colomban C, Micallef J, Lefebvre MN, Dubourg O, Gonnaud PM, Stojkovic T, et al. Clinical spectrum and gender differences in a large cohort of Charcot-Marie-Tooth type 1A patients. J Neurol Sci. (2014) 336:155–60. doi: 10.1016/j.jns.2013.10.029 PubMed Abstract | CrossRef Full Text | Google Scholar 10. Schuurmans MM, Schneider-Yin X, Rüfenacht UB, Schnyder C, Minder CE, Puy H, et al. Influence of age and gender on the clinical expression of acute intermittent porphyria based on molecular study of porphobilinogen deaminase gene among Swiss patients. Mol Med. (2001) 7:535–42. doi: 10.1007/BF03401859 PubMed Abstract | CrossRef Full Text | Google Scholar 11. Mattina A, Giammanco A, Giral P, Rosenbaum D, Carrié A, Cluzel P, et al. Polyvascular subclinical atherosclerosis in familial hypercholesterolemia: the role of cholesterol burden and gender. Nutr Metab Cardiovasc Dis. (2019) 29:1068–76. doi: 10.1016/j.numecd.2019.06.015 PubMed Abstract | CrossRef Full Text | Google Scholar 12. Cohen R, Halevy A, Aharon S, Shuper A. Attention deficit hyperactivity disorder in neurofibromatosis type 1: evaluation with a continuous performance test. J Clin Neurol. (2018) 14:153–7. doi: 10.3988/jcn.2018.14.2.153 PubMed Abstract | CrossRef Full Text | Google Scholar 13. Jacobi H, Bauer P, Giunti P, Labrum R, Sweeney MG, Charles P, et al. The natural history of spinocerebellar ataxia type 1, 2, 3, and 6: a 2-year follow-up study. Neurology. (2011) 77:1035–41. doi: 10.1212/WNL.0b013e31822e7ca0 PubMed Abstract | CrossRef Full Text | Google Scholar 14. Jacobi H, du Montcel ST, Bauer P, Giunti P, Cook A, Labrum R, et al. Long-term disease progression in spinocerebellar ataxia types 1, 2, 3, and 6: a longitudinal cohort study. Lancet Neurol. (2015) 14:1101–8. doi: 10.1016/S1474-4422(15)00202-1 PubMed Abstract | CrossRef Full Text | Google Scholar 15. Klockgether T, Lüdtke R, Kramer B, Abele M, Bürk K, Schöls L, et al. The natural history of degenerative ataxia: a retrospective study in 466 patients. Brain. (1998) 121:589–600. doi: 10.1093/brain/121.4.589 PubMed Abstract | CrossRef Full Text | Google Scholar 16. França MC Jr, D'Abreu A, Nucci A, Cendes F, Lopes-Cendes I. Progression of ataxia in patients with Machado-Joseph disease. Mov Disord. (2009) 24:1387–90. doi: 10.1002/mds.22627 PubMed Abstract | CrossRef Full Text | Google Scholar 17. Pradotto L, Mencarelli M, Bigoni M, Milesi A, Di Blasio A, Mauro A. Episodic ataxia and SCA6 within the same family due to the D302N CACNA1A gene mutation. J Neurol Sci. (2016) 371:81–4. doi: 10.1016/j.jns.2016.10.029 PubMed Abstract | CrossRef Full Text | Google Scholar 18. Li F, Li QQ, Tan ZX, Zhang SY, Liu J, Zhao EY, et al. A novel mutation in CACNA1S gene associated with hypokalemic periodic paralysis which has a gender difference in the penetrance. J Mol Neurosci. (2012) 46:378–83. doi: 10.1007/s12031-011-9596-1 PubMed Abstract | CrossRef Full Text | Google Scholar 19. Ishikawa I, Maeda K, Nakai S, Kawaguchi Y. Gender difference in the mean age at the induction of hemodialysis in patients with autosomal dominant polycystic kidney disease. Am J Kidney Dis. (2000) 35:1072–5. doi: 10.1016/S0272-6386(00)70042-4 PubMed Abstract | CrossRef Full Text | Google Scholar 20. Gretz N, Ceccherini I, Kränzlin B, Klöting I, Devoto M, Rohmeiss P, et al. Gender-dependent disease severity in autosomal polycystic kidney disease of rats. Kidney Int. (1995) 48:496–500. doi: 10.1038/ki.1995.319 PubMed Abstract | CrossRef Full Text | Google Scholar 21. Kyriakou A, Savva SC, Savvides I, Pangalou E, Ioannou YS, Christou S, et al. Gender differences in the prevalence and severity of bone disease in thalassaemia. Pediatr Endocrinol. (2008) 6(Suppl 1):116–22. PubMed Abstract | Google Scholar 22. Walker FO. Huntington's disease. Lancet. (2007) 369:218–28. doi: 10.1016/S0140-6736(07)60111-1 PubMed Abstract | CrossRef Full Text | Google Scholar 23. Telenius H, Kremer HP, Theilmann J, Andrew SE, Almqvist E, Anvret M, et al. Molecular analysis of juvenile Huntington disease: the major influence on (CAG)n repeat length is the sex of the affected parent. Hum Mol Genet. (1993) 2:1535–40. doi: 10.1093/hmg/2.10.1535 PubMed Abstract | CrossRef Full Text | Google Scholar 24. Trottier Y, Biancalana V, Mandel JL. Instability of CAG repeats in Huntington's disease: relation to parental transmission and age of onset. J Med Genet. (1994) 31:377–82. doi: 10.1136/jmg.31.5.377 PubMed Abstract | CrossRef Full Text | Google Scholar 25. Nørremølle A, Sørensen SA, Fenger K, Hasholt L. Correlation between magnitude of CAG repeat length alterations and length of the paternal repeat in paternally inherited Huntington's disease. Clin Genet. (1995) 47:113–7. doi: 10.1111/j.1399-0004.1995.tb03941.x PubMed Abstract | CrossRef Full Text | Google Scholar 26. Aziz NA, van Belzen MJ, Coops ID, Belfroid RD, Roos RA. Parent-of-origin differences of mutant HTT CAG repeat instability in Huntington's disease. Eur J Med Genet. (2011) 54:413–8. doi: 10.1016/j.ejmg.2011.04.002 PubMed Abstract | CrossRef Full Text | Google Scholar 27. Simard O, Grégoire MC, Arguin M, Brazeau MA, Leduc F, Marois I, et al. Instability of trinucleotidic repeats during chromatin remodeling in spermatids. Hum Mutat. (2014) 35:1280–4. doi: 10.1002/humu.22637 PubMed Abstract | CrossRef Full Text | Google Scholar 28. Duyao M, Ambrose C, Myers R, Novelletto A, Persichetti F, Frontali M, et al. Trinucleotide repeat length instability and age of onset in Huntington's disease. Nat Genet. (1993) 4:387–92. doi: 10.1038/ng0893-387 PubMed Abstract | CrossRef Full Text | Google Scholar 29. Illarioshkin SN, Igarashi S, Onodera O, Igarashi S, Onodera O, Markova ED, et al. Trinucleotide repeat length and rate of progression of Huntington's disease. Ann Neurol. (1994) 36:630–5. doi: 10.1002/ana.410360412 PubMed Abstract | CrossRef Full Text | Google Scholar 30. Brandt J, Bylsma FW, Gross R, Stine OC, Ranen N, Ross CA. Trinucleotide repeat length and clinical progression in Huntington's disease. Neurology. (1996) 46:527–31. doi: 10.1212/WNL.46.2.527 PubMed Abstract | CrossRef Full Text | Google Scholar 31. Zielonka D, de Mezer M, Niezgoda A, Reperowicz K, Krzyzosiak W, Kozubski W. Clinical picture of patients with Huntington's disease in relation to the number of trinucleotide CAG repeats in IT-15 gene. Neurol Neurochir Pol. (2002) 36:903–9. PubMed Abstract | Google Scholar 32. Claes S, Van Zand K, Legius E, Dom R, Malfroid M, Baro F, et al. Correlations between triplet repeat expansion and clinical features in Huntington's disease. Arch Neurol. (1995) 52:749–53. doi: 10.1001/archneur.1995.00540320021009 PubMed Abstract | CrossRef Full Text | Google Scholar 33. Ashizawa T, Wong LJ, Richards CS, Caskey CT, Jankovic J. CAG repeat size and clinical presentation in Huntington's disease. Neurology. (1994) 44:1137–43. doi: 10.1212/WNL.44.6.1137 PubMed Abstract | CrossRef Full Text | Google Scholar 34. Kieburtz K, MacDonald M, Shih C, Feigin A, Steinberg K, Bordwell K, et al. Trinucleotide repeat length and progression of illness in Huntington's disease. J Med Genet. (1994) 31:872–4. doi: 10.1136/jmg.31.11.872 PubMed Abstract | CrossRef Full Text | Google Scholar 35. Kremer B, Clark CM, Almqvist EW, Raymond LA, Graf P, Jacova C, et al. Influence of lamotrigine on progression of early Huntington disease: a randomized clinical trial. Neurology. (1999) 53:1000–11. doi: 10.1212/WNL.53.5.1000 PubMed Abstract | CrossRef Full Text | Google Scholar 36. Rosenblatt A, Liang KY, Zhou H, Abbott MH, Gourley LM, Margolis RL, et al. The association of CAG repeat length with clinical progression in Huntington disease. Neurology. (2006) 66:1016–20. doi: 10.1212/01.wnl.0000204230.16619.d9 PubMed Abstract | CrossRef Full Text | Google Scholar 37. Ravina B, Romer M, Constantinescu R, Biglan K, Brocht A, Kieburtz K, et al. The relationship between CAG repeat length and clinical progression in Huntington's disease. Mov Disord. (2008) 23:1223–7. doi: 10.1002/mds.21988 PubMed Abstract | CrossRef Full Text | Google Scholar 38. Zielonka D, Niezgoda A, Olejniczak M, Krzyzosiak W, Marcinkowski J, Kozubski W. Gender differences in the CAG repeats and clinical picture correlations in Huntington's disease. Ceska Slovenska Neurol Neurochirurg. (2008) 71:688–94. 39. Zielonka D, Marinus J, Roos RA, De Michele G, Di Donato S, Putter H, et al. The influence of gender on phenotype and disease progression in patients with Huntington's disease. Parkinsonism Relat Disord. (2013) 19:192–7. doi: 10.1016/j.parkreldis.2012.09.012 PubMed Abstract | CrossRef Full Text | Google Scholar 40. Handley O, van Walsem M, Juni P, Bachoud-Levi AC, Bentivoglio AR, Bonelli RM, et al. Study protocol of registry e version 2.0-European Huntington's disease network (EHDN). Hygeia Public Health. (2011) 46:115–82. Google Scholar 41. Penney JB Jr, Vonsattel JP, MacDonald ME, Gusella JF, Myers RH. CAG repeat number governs the development rate of pathology in Huntington's disease. Ann Neurol. (1997) 41:689–92. doi: 10.1002/ana.410410521 PubMed Abstract | CrossRef Full Text | Google Scholar 42. Brinkman RR, Mezei MM, Theilmann J, Almqvist E, Hayden MR. The likelihood of being affected with Huntington disease by a particular age, for a specific CAG size. Am J Hum Genet. (1997) 60:1202–10. PubMed Abstract | Google Scholar 43. Djousse L, Knowlton B, Hayden M, Almqvist EW, Brinkman R, Ross C, et al. Interaction of normal and expanded CAG repeat sizes influences age at onset of Huntington disease. Am J Med Genet. (2003) 119A:279–82. doi: 10.1002/ajmg.a.20190 PubMed Abstract | CrossRef Full Text | Google Scholar 44. Zielonka D, Ren M, De Michele G, Roos RA, Squitieri F, Bentivoglio AR, et al. The contribution of gender differences in motor, behavioral and cognitive features to functional capacity, independence and quality of life in patients with Huntington's disease. Parkinsonism Relat Disord. (2018) 49:42–7. doi: 10.1016/j.parkreldis.2018.01.006 PubMed Abstract | CrossRef Full Text | Google Scholar 45. Marder K, Zhao H, Myers RH, Cudkowicz M, Kayson E, Kieburtz K, et al. Rate of functional decline in Huntington's disease. Huntington Study Group. Neurology. (2000) 54:452–8. doi: 10.1212/WNL.54.2.452 PubMed Abstract | CrossRef Full Text | Google Scholar 46. Peavy GM, Jacobson MW, Goldstein JL, Hamilton JM, Kane A, et al. Cognitive and functional decline in Huntington's disease: dementia criteria revisited. Mov Disord. (2010) 25:1163–9. doi: 10.1002/mds.22953 PubMed Abstract | CrossRef Full Text | Google Scholar 47. Bylsma FW, Rothlind J, Hall MR, Folstein SE, Brandt J. Assessment of adaptive functioning in Huntington's disease. Mov Disord. (1993) 8:183–90. doi: 10.1002/mds.870080212 PubMed Abstract | CrossRef Full Text | Google Scholar 48. Mahant N, McCusker EA, Byth K, Graham S, Huntington Study G. Huntington's disease: clinical correlates of disability and progression. Neurology. (2003) 61:1085–92. doi: 10.1212/01.WNL.0000086373.32347.16 PubMed Abstract | CrossRef Full Text | Google Scholar 49. Bruzelius E, Scarpa J, Zhao Y, Basu S, Faghmous JH, Baum A. Huntington's disease in the United States: variation by demographic and socioeconomic factors. Mov Disord. (2019) 34:858–65. doi: 10.1002/mds.27653 PubMed Abstract | CrossRef Full Text | Google Scholar 50. Dorner JL, Miller BR, Barton SJ, Brock TJ, Rebec GV. Sex differences in behavior and striatal ascorbate release in the 140 CAG knock-in mouse model of Huntington's disease. Behav Brain Res. (2007) 178:90–7. doi: 10.1016/j.bbr.2006.12.004 PubMed Abstract | CrossRef Full Text | Google Scholar 51. Bode FJ, Stephan M, Suhling H, Pabst R, Straub RH, Raber KA, et al. Sex differences in a transgenic rat model of Huntington's disease: decreased 17β-estradiol levels correlate with reduced numbers of DARPP32+ neurons in males. Hum Mol Genet. (2008) 17:2595–609. doi: 10.1093/hmg/ddn159 PubMed Abstract | CrossRef Full Text | Google Scholar 52. Kuljis DA, Gad L, Loh DH, MacDowell Kaswan Z, Hitchcock ON, Ghiani CA, et al. Sex differences in circadian dysfunction in the BACHD mouse model of Huntington”s disease. PLoS One. (2016) 11:e0147583. doi: 10.1371/journal.pone.0147583 PubMed Abstract | CrossRef Full Text | Google Scholar 53. Padovan-Neto FE, Jurkowski L, Murray C, Stutzmann GE, Kwan M, Ghavami A, et al. Age- and sex-related changes in cortical and striatal nitric oxide synthase in the Q175 mouse model of Huntington's disease. Nitric Oxide. (2019) 83:40–50. doi: 10.1016/j.niox.2018.12.002 PubMed Abstract | CrossRef Full Text | Google Scholar 54. Zielonka D, Witkowski G, Puch EA, Lesniczak M, Mazur-Michalek I, Isalan M, et al. Prevalence of non-psychiatric comorbidities in pre-symptomatic and symptomatic Huntington's disease gene carriers in Poland. Front Med. (2020) 7:79. doi: 10.3389/fmed.2020.00079 PubMed Abstract | CrossRef Full Text | Google Scholar 55. Rangel-Barajas C, Rebec GV. Overview of Huntington's disease models: neuropathological, molecular, behavioral differences. Curr Protoc Neurosci. (2018) 83:e47. doi: 10.1002/cpns.47 PubMed Abstract | CrossRef Full Text | Google Scholar 56. Markianos M, Panas M, Kalfakis N, Vassilopoulos D. Plasma testosterone in male patients with Huntington's disease: relations to severity of illness and dementia. Ann Neurol. (2005) 57:520–5. doi: 10.1002/ana.20428 PubMed Abstract | CrossRef Full Text | Google Scholar 57. Markianos M, Panas M, Kalfakis N, Vassilopoulos D. Plasma testosterone, dehydroepiandrosterone sulfate, and cortisol in female patients with Huntington's disease. Neuro Endocrinol Lett. (2007) 28:199–203. PubMed Abstract | Google Scholar 58. Lee JK, Ding Y, Conrad AL, Cattaneo E, Epping E, Mathews K, et al. Sex-specific effects of the Huntington gene on normal neurodevelopment. J Neurosci Res. (2017) 95:398–408. doi: 10.1002/jnr.23980 PubMed Abstract | CrossRef Full Text | Google Scholar 59. Meoni S, Macerollo A, Moro E. Sex differences in movement disorders. Nat Rev Neurol. (2020) 16:84–96. doi: 10.1038/s41582-019-0294-x PubMed Abstract | CrossRef Full Text | Google Scholar 60. Horstink MW, Strijks E, Dluzen DE. Estrogen and Parkinson's disease. Adv Neurol. (2003) 91:107–14. PubMed Abstract | Google Scholar 61. Quinn NP, Marsden CD. Menstrual- related fluctuations in Parkinson's disease. Mov Disord. (1986) 1:85–7. doi: 10.1002/mds.870010112 PubMed Abstract | CrossRef Full Text | Google Scholar 62. Sandyk R. Estrogens and the pathophysiology of Parkinson's disease. Int J Neurosci. (1989) 45:119–22. doi: 10.3109/00207458908986223 PubMed Abstract | CrossRef Full Text | Google Scholar 63. Gwinn-Hardy KA, Adler CH, Weaver AL, Fish NM, Newman SJ. Effect of hormone variations and other factors on symptom severity in women with dystonia. Mayo Clin Proc. (2000) 75:235–40. doi: 10.4065/75.3.235 PubMed Abstract | CrossRef Full Text | Google Scholar 64. Tsang KL, Ho SL, Lo SK. Estrogen improves motor disability in Parkinsonian postmenopausal women with motor fluctuations. Neurology. (2000) 54:2292–8. doi: 10.1212/WNL.54.12.2292 PubMed Abstract | CrossRef Full Text | Google Scholar Keywords: Huntington's disease, gender differences, sex contribution, autosomal, monogenetic Citation: Zielonka D and Stawinska-Witoszynska B (2020) Gender Differences in Non-sex Linked Disorders: Insights From Huntington's Disease. Front. Neurol. 11:571. doi: 10.3389/fneur.2020.00571 Received: 04 March 2020; Accepted: 19 May 2020; Published: 07 July 2020. Edited by: Reviewed by: Copyright © 2020 Zielonka and Stawinska-Witoszynska. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Daniel Zielonka, [email protected]
Published: 16 May 2020
Reactions Weekly, Volume 1804, pp 220-220; https://doi.org/10.1007/s40278-020-78646-y

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Journal of Communications and Networks, Volume 22, pp 162-168; https://doi.org/10.1109/jcn.2020.100012

Abstract:
Journal of Communications and Networks (JCN) was launched in March 1999. It is published six times per year, and is committed to publishing high quality papers that advance the state-of-the-art and practical applications of communications and information networks. Theoretical research contributions presenting new techniques, concepts, or analyses, applied contributions reporting on experiences and experiments, and tutorial expositions of permanent reference value are welcome. The subjects covered by this journal include all topics in communication theory and techniques, communication systems, and information networks. JCN is indexed in Science Citation Index Expanded (SCIE), Engineering Village and Scopus. The official title is Journal of Communications and Networks (JCN) and the abbreviate title is ‘J.Commn.Net’.
Published: 25 April 2020
Reactions Weekly, Volume 1801, pp 219-219; https://doi.org/10.1007/s40278-020-77879-7

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Published: 4 January 2020
Reactions Weekly, Volume 1785, pp 372-372; https://doi.org/10.1007/s40278-020-73353-5

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Sergi Roig Puiggros, , , Nadia I. Bocai, , , Gimena Gomez, Sara Grassi, , , et al.
Published: 20 October 2019
Journal of Neurochemistry, Volume 153, pp 10-32; https://doi.org/10.1111/jnc.14900

Abstract:
Perception of our environment entirely depends on the close interaction between the central and peripheral nervous system. In order to communicate each other, both systems must develop in parallel and in coordination. During development, axonal projections from the CNS as well as the PNS must extend over large distances to reach their appropriate target cells. To do so, they read and follow a series of axon guidance molecules. Interestingly, while these molecules play critical roles in guiding developing axons, they have also been shown to be critical in other major neurodevelopmental processes, such as the migration of cortical progenitors. Currently, a major hurdle for brain repair after injury or neurodegeneration is the absence of axonal regeneration in the mammalian CNS. By contrasts, PNS axons can regenerate. Many hypotheses have been put forward to explain this paradox but recent studies suggest that hacking neurodevelopmental mechanisms may be the key to promote CNS regeneration. Here we provide a seminar report written by trainees attending the second Flagship school held in Alpbach, Austria in September 2018 organized by the International Society for Neurochemistry (ISN) together with the Journal of Neurochemistry (JCN). This advanced school has brought together leaders in the fields of neurodevelopment and regeneration in order to discuss major keystones and future challenges in these respective fields.
Untung Sujianto, Andrew Johan
Published: 9 October 2019
KnE Life Sciences pp 162–175-162–175; https://doi.org/10.18502/kls.v4i13.5237

Abstract:
[1] Therapies, C. (2014). Complementary, Alternative, and Traditional Therapies, 34(6), 50–56. [2] Gronowicz,G.,Secor,E.,Flynn,J.,Jellison,E.,&Kuhn,L.(2015).TherapeuticTouchhassignificanteffects onmouseBreastCancermetastasisandImmuneresponsesbutnotprimarytumorsize.Evidence-Based Complementary and Alternative Medicine, 2015, 1–10. https://doi.org/10.1155/2015/926565 [3] Ross, S., & Grande, R. (2017). A Practice-Based Theory of Healing Through Therapeutic Touch, 1–13 [4] Gregory,S.,&Verdouw,J.(2005).Therapeutictouch:Itsapplicationforresidentsinagedcare.Australian Nursing Journal (July 1993), 12(7), 23–25. Retrieved from http://www.healthyoutlook.com.au/images/ 0502_clin_update.pdf [5] JeanSayre-AdamsStephenWright.(2001).TherapeuticTouch(2ndEditio).Livingstone:ElsevierMosby Publications [6] Robinson, J., Fc, B., & Dolk, H. (2009). Therapeutic touch for anxiety disorders (Review), (3). [7] Myers,C.D.,Walton,T.,Bratsman,L.,Wilson,J.,&Small,B.(2008).MassageModalitiesandSymptoms Reported by Cancer Patients: Narrative Review, 6(1), 19–28. https://doi.org/10.2310/7200.2008.0005 [8] Study,A.Q.(2015).EffectsofTherapeuticTouchonAnxiety,VitalAmericanHolisticNursesAssociation Volume30Number 4 Signs,and CardiacDysrhythmiain a Sample ofDecember2012 225-234© The Author(s) 2012 Iranian Women Undergoing Cardiac 10.1177/0898010112453325 http:, 225–234. [9] Jung,M., Jonides,J., Northouse,L., Berman,M. G., Koelling,T.M.,& Pressler,S. J.(2017).Randomized Crossover Study of the Natural Restorative Environment Intervention to Improve Attention and Mood in Heart Failure. Journal of Cardiovascular Nursing, 32(5), 464–479. https://doi.org/10.1097/JCN. 0000000000000368 [10] Taylor,A.F.,Kuo,F.E.,&Sullivan,W.C.(2002).Viewsofnatureandself-discipline:Evidencefrominner city children. Journal of Environmental Psychology, 22(1–2), 49–63. https://doi.org/10.1006/jevp.2001. 0241 [11] Alaaeddine, N., Okais, J., Ballane, L., & Baddoura, R. M. (2012). Use of complementary and alternative therapy among patients with rheumatoid arthritis and osteoarthritis, 1–7. https://doi.org/10.1111/j.13652702.2012.04169.x [12] Unit, D., Diego, S., Awdishu, L., & Diego, S. (2018). Copyright of Nephrology Nursing Journal is the property of American Nephrology Nurses ’ Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder ’ s express written permission. However, users may print, download, or email articles for individual use., 45(2). [13] Aghabati, N., Mohammadi, E., & Esmaiel, Z. P. (2010). The Effect of Therapeutic Touch on Pain and Fatigue of Cancer Patients Undergoing Chemotherapy, 7(February 2008), 375–381. https://doi.org/10. 1093/ecam/nen006. [14] Article, O., Sancar, B., Yalcin, A. S., & Acikgoz, I. (2018). An examination of anxiety levels of nursing students caring for patients in terminal period, 34(1), 94–99. [15] Moeini, M., Zare, Z., Hazrati, M., & Saghaei, M. (2008). Original Article Effect of therapeutic touch on patients’ anxiety before coronary artery bypass graft surgery, 13(2), 47–51. [16] Tabatabaee, A., Tafreshi, M., Rassouli, M., Aledavood, S., Majd, H., & Farahmand, and. (2016a). Effect of Therapeutic Touch in Patients with Cancer: a Literature Review. Medical Archives, 70(2), 142. https: //doi.org/10.5455/medarh.2016.70.142-147 [17] Tabatabaee, A., Tafreshi, M., Rassouli, M., Aledavood, S., Majd, H., & Farahmand, and. (2016b). Effect of Therapeutic Touch in Patients with Cancer: a Literature Review. Medical Archives, 70(2), 142. https: //doi.org/10.5455/medarh.2016.70.142-147 [18] Karagozoglu, S., & Kahve, E. (2013). Effects of back massage on chemotherapy-related fatigue and anxiety: Supportive care and therapeutic touch in cancer nursing. Applied Nursing Research, 26(4), 210–217. https://doi.org/10.1016/j.apnr.2013.07.002
Journal of Christian Nursing, Volume 36, pp 73-73; https://doi.org/10.1097/cnj.0000000000000597

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Have you discovered What's Vital yet? This new, regular JCN column probes a Scripture portion, considering the cultural facet and biblical context. Author Jan Wilson writes the content and applies a nursing perspective by concluding the column with questions for nurses to consider. The first column addressed crossing racial, ethnic, religious, and social barriers to communicate with others different from ourselves. In this issue, Wilson considers how rest—frequently a commodity in short supply—may be experienced in unexpected places. This addition to JCN can also be useful as a discussion topic or Bible study focus for nurses' groups. Let us know your opinion of this new column and how you're using it. As the only nursing journal focused on practicing from a biblically-based, Christian perspective, JCN welcomes new and returning authors to submit manuscripts. The instructions for authors detail the types of articles published, how to submit your article, and the appropriate writing style. All articles are submitted online; a tutorial at the JCN editorial manager site walks first-time authors through this process. Authors are encouraged to submit articles for columns, as well as longer manuscripts related to faith community nursing, healthcare missions, clinical topics for practicing nurses and educators, original research, and professional issues. Substantive, referenced teaching articles can be selected for the issue's CE feature; the publisher produces the CE test. Articles typically are published 9 to 12 months after acceptance; revision may be needed, and editorial staff provide guidance with this process. All editing changes of accepted manuscripts are submitted for author approval prior to publication, whether in print or online. To access the instructions for authors and other detailed information about the types of articles published in JCN, go to https://www.editorialmanager.com/ncf-jcn/default.aspx. Encouragement, ideas, successes, pertinent input from the Bible. These are all found on the NCF blog at ncf-jcn.org. This smorgasbord of articles, devotionals, news, and interviews brings into focus local nurse groups that are impacting peers and their communities; student groups and faculty advisors leaping out in faith; and interviews with NCF staff. Scripture-based content and guest posts from NCFI, our international sister organization, are served, along with thoughtful content from JCN authors. Recently a candid, personal post by JCN contributing editor Kris Mauk on what she gained through physical pain brought this response from a reader: I echo praise to God for teaching me gratitude for the simple things of life, like taking walks and picking flowers, after years of limitations due to severe back pain and numerous surgeries. His grace has turned loss into gain. Chronic pain continues to teach me to be grateful for times of relief and helps me be sensitive to the needs of others. You can subscribe by email to the blog feed using the button on the blog page. Posts release every 6 to 10 days. Visit the ncf-jcn.org website and see what you think. International Westberg Symposium, “In These Times: Serving Through Adversity” is April 8-10, 2019, at First Baptist Broad Church in Memphis, Tennessee. Find details here: https://westberginstitute.org/symposium2019/. Trinity Western University School of Nursing Faith & Nursing Symposium, “Inspiring Person-Centeredness: A Call to Action,” May 8-10, 2019, held at TWU, near Vancouver, British Columbia, Canada. Get more information at: https://www.twu.ca/inspiring-person-centredness-call-action.
Journal of Christian Nursing, Volume 36, pp 70-71; https://doi.org/10.1097/cnj.0000000000000600

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“It's about relationship with each other” is how Christy Secor, DNP, RN, CDWF, summarizes why NCF nurse groups are so beneficial. As NCF's Professional Ministries Director, Christy supports and helps develop these groups across the United States. “One of the best ways we have to relate to each other as professional nurses is through NCF nurse groups,” Christy says. Nurse groups, which can meet at a workplace, an individual's home, a coffee shop, or nearly anywhere, provide a gathering opportunity for prayer, Bible study, and personal support. For examples of how nurse groups are building relationships, read on the NCF blog (ncf.-jcn.org/blog) about groups in Delaware and Northern California. If you don't have a group near where you live or work, email [email protected] so NCF staff can work with you to find or start a group. NCF is unique! As a ministry and professional organization, NCF serves nursing students, nurse educators, and practicing nurses—the only organization of its kind. As a ministry organization, NCF brings faith to students in the struggle of nursing school and to nurses in the challenges of practice or academia. As a professional organization, we bring expert nursing knowledge to every member through the Journal of Christian Nursing, ANCC-certified continuing education, and more. Why join NCF? Your membership indicates your commitment to faith and the profession on your résumé and to your employer. Membership in NCF is economical—less than other nursing organizations. Your membership helps support the growth of NCF chapters in nursing schools and NCF nurse groups throughout the country. Another reason to join NCF? The benefits! Members receive the award-winning JCN, free and discounted CE in numerous clinical areas, including pharmacology hours for advanced practice nurses, discounts from LWW and InterVarsity publishers, a network of fellow Christian nurses throughout the country, and more. Join NCF today at http://ncf-jcn.org/membership. Already a member? Watch your inbox for your next renewal notice. Remembering Our Purpose Have you ever asked God, Why? Why am I here? Am I making a difference? What do you want of me? There is not enough of me to get through this day...this shift...this semester. It's too easy within the chaos of life and work to lose our sense of purpose in who we are as individuals and as professionals. Sometimes we lose hope. As NCF's Professional Ministries Director, I speak with nursing students, educators, and nurses in practice about the challenges they face. The demands are real. Long hours, doing more with less, not having time for our patients, a lack of teamwork, insecurities about pay, caring for our loved ones, as well as coping with the loss and grief we experience. It is into this space Nurses Christian Fellowship is called. It is into this space we as nurses are called. The mission statement for NCF reads: The purpose of Nurses Christian Fellowship ... is to establish and advance in nursing, within education and practice, witnessing communities of nursing students and nurses who follow Jesus as Savior and Lord: growing in love for God, God's Word, God's people of every ethnicity and culture, and God's purposes in the world. Our purpose is always found in God. It isn't one more thing for us to do. It is who we are. The beauty of God's design is this ... our purpose is lived in relationship with him and with each other.
Published: 11 January 2019
Frontiers in Neuroscience, Volume 12; https://doi.org/10.3389/fnins.2018.01032

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Editorial on the Research TopicNeurocardiovascular Diseases: New Aspects of the Old Issues Concept of neuro-cardiovascular diseases (NCVD) is one fruitful approach that enables the comprehension of the pathological processes raising along the brain-heart & blood vessel axes from the integrative prospective. That is the reason why multidisciplinary and interdisciplinary approaches result in novel insights into pathophysiological processes bringing new directions for therapeutic development, both at the laboratory bench and at the clinical bedside. NCVD comprises the group of pathologies that have as a primary pathological substrate the changes in neurochemical, neurophysiological and neuroanatomical levels of the autonomic nervous system (ANS) and its regulated organs (e.g., heart, blood vessels). A striking fact is that “cardiovascular disease is the leading cause of death in the world today and will remain so by the year 2020” (The WHO MONICA Project, Investigators, 1988) strongly supports the need for new insights into cardiovascular regulatory mechanisms (Bojić, 2003). A picture of the classical cardiovascular risk factors from the prospective of neural cardiovascular control, links these factors with stress. The central topic of NCV physiology is related to stress-induced disfunction (e.g., hypertension, Du et al., 2017), emotional stress coping-cigarette smoking, obesity, Strickland et al., 2007) and stress-releasing strategies (exercise, Acevedo et al., 2006; Webb et al., 2017). Central questions of neurophysiology of stress is: a. Identification of different brain networks activated during stress b. Spacial and temporal patterns of their activation c. Identification of neuronal hubs coupling cognitive-emotional neural networks with body effectors, like the hypothalamus-pituitary-adrenal (HPA) and the sympathetic-adrenomedullary axis (SAM) (Ulrich-Lai and Herman, 2009; Godoy et al., 2018). The interaction of the heart and vessels with the central and peripheral nervous systems represents the major topic of the basic neuro-cardiovascular research, with the current aim of the field being to highlight the mechanisms of devastating effects of stress upon the cardiovascular system. In the review of Dampney the focus was on anatomical basis and functional role of the dorsolateral periaqueductal gray (dlPAG) in generating behavioral and autonomic responses to real and perceived emotional stressors. Central position and integratory function of this structure, between higher cortical regions (auditory, secondary visual, olphactory; medial prefrontal cortex -MPC, hypothalamus and lower brainstem structures), qualifies it as a crucial, emotional stress mediator neural hub. Though the review reports, parallel and comparative results obtained in different species, the guiding theme is the enlightening of the understanding of the morpho-functional substrate affiliated with emotional stress response in humans. Crucial for understanding of the behavioral response to the perceived emotional stress are the inputs from the MPC to the dlPAG. The species-specific MPC subregion of the primate brain, the area 10 m, has the largest volume in humans with respect to other primate species. Allman and collaborates (Allman et al., 2002) describe this structure as a comparator of current and memorized behavioral states, and as a consequent decision-maker about future, potentially advantageous behavior. Its high activation and direct interface with dlPAG points to its special role in complex emotional responses, resulting from such comparisons. In conclusion, dlPAG is presented as integrator of the reactions of both the conscious and the unconscious to threatening stimuli with dependent autonomic networks (i.e., cardiorespiratory network) which support the behavioral response to stress and threatening stimuli. Future studies need to address the questions of chemical phenotyping of the dlPAG and other extensive stress mediating brain networks; the question of morpho-functional plasticity with respect to timing and duration of stress exposure; association of stress-induced morpho-functional changes of critical brain networks associated with different cardiovascular pathologies, as well as the question of genetic predisposition to developing of specific pathological entities like NCVD on the order of short or long time scales. The impact of peripheral information on neural mediation of the cardiovascular (CVS) response can be crucial to the development of pathologies initiated by physical stressors like injury. When a physical stressor is recognized by the brainstem through pain, inflammation and other signals, both fast SAM and sluggish HPA responses are activated (Godoy et al., 2018). Up to the present it is not known what the role of pre-stimulus of the ANS regime is for development of the compartment syndrome. In this line, the study that investigated the effect of peripheral neural input to the heart rate regulating network by Watanabe and Hotta for the first time examined the specific cardiac autonomic changes induced by the bio-mechanical pressure stimulation of skeletal muscle. The authors identified sympathetic nervous system as the effector of changes in heart rate and blood pressure. In addition, it was demonstrated that the tonic level of pre-stimulus sympathetic neural activity determines the direction of induced heart rate changes and changes to blood pressure. This data could be of particular importance for understanding the compartment syndrome, the condition of muscle ischemic necrosis due to excessive intramuscular pressure and blood hypoperfusion. Future studies are necessary for revealing the site of the interaction of peripheral muscle pressure, stretching and contraction stimulus of the CVS neural networks (spinal or brainstem), and chemical phenotyping for the purpose of pharmacological intervention, and the role of tonic pre-stimulus sympathetic neural activity, for the development of the hemodynamic profiles that are susceptible to progression of the compartment syndrome. Drug targeting of spinal and brainstem autonomic neural circuits causally involved in the genesis of different NCVD could result with long-awaited pharmacological solutions (Pierce et al., 2010; Zimmerman, 2011) for unexpected groups of pathophysiological entities, like compartment syndrome and neurogenic hypertension. Neurogenic hypertension (NH) and vasovagal syncope (VVS) are NCV entities representing an unsolved pathophysiological puzzle. Novel data exists about the central molecular mechanisms regulating the tonic activity of preganglionic sympathetic neurons (Zimmerman, 2011), with antagonizing effects of the angiotensin II receptor and the MAS1 receptor mediated cascade offer a promising perspective for in silico strategies for investigation of NH. The Information Spectrum Method (ISM), a virtual spectroscopy method for studying the long-range interactions between biological macromolecules (Veljković et al., 1985), was previously successfully applied in study of HIV (Veljković et al., 2007), anthrax (Doliana et al., 2008) and the influenza virus (Perović et al., 2013). This widely accepted method (Veljković et al., 2011) was applied for the first time by Bojić et al. for the investigation of molecular targets of NH and VVS. As the result of this study, there have been proposed three novel therapeutic candidates for treatment of NH (apelin-28, apelin-31 and apelin-36) and also 12 repurposed antimuscarinic drugs potentially could be efficient in VVS treatment. Follow up with in vitro and in vivo studies will test the therapeutic capacities of drug candidates identified by ISM. Baroreflex sensitivity (BRS) represents one of the central research topics of NCV physiology in the last few decades (Bojić, 2003; Silvani et al., 2003, 2005; Zoccoli et al., 2005; Bajić et al., 2010; Kapidžić et al., 2014; Platiša et al.) and pathophysiology (Parati et al., 2004; Glišić et al., 2016). BRS has been recognized as valuable prognostic factor for the outcome of different NCVD like myocardial infarction (La Rovere et al., 1998), heart failure (Libbus et al., 2016) and hypertension (Subha et al., 2016). The methodology of BRS estimation evolved from the classical methods of BRS estimation based on induced blood pressure changes (mechanical, pharmacological, etc.) with the related research activity achieving considerable levels since the late 1950's (Ernsting and Parry, 1957; Lamberti et al., 1968; Kirchheim, 1976), up to the BRS techniques for analysis of blood pressure and HP spontaneous fluctuations, which were introduced during the late 1980's (Fritsch et al., 1986; Bojić, 2003). Major advantages of spontaneous fluctuations method are: 1. There is no administration of vasoactive compounds or external appliances that could influence the baroreceptor reflex by a direct action on receptor or effector sites (Coleman, 1980). 2. BRS is measured within physiological ABP ranges, allowing the computation of the gain at ABP close to the operating set point value, with minimal nonspecific effects from other efferent nerves. 3. The method does not arouse subjects or animals, thereby reducing stress induced effects. 4. In contrast with pharmacological or mechanical methods, they are suitable to assess the BRS over prolonged periods of time (Mancia and Mark, 1983; Oosting et al., 1997). Still, time and frequency domain analysis of ANS activity in cardiovascular signals require certain conditions, with a stable baseline as one of the most important and most difficult states to obtain on long data segments. Li et al. propose a Multiple Trigonometric Regressive Spectral Analysis as a novel method for baroreflex sensibility (BRS) estimation for short (20–30's) time segments. The proposed method uses the oscillations of ABP and HP instead of their original values. The method provides reliable estimates of BRS without regard to posture change during the short data segments of 20–30 s in length. The proposed method solves several shortcomings of the sequence method by increasing the accuracy and validity of BRS estimation (Ziemssen et al., 2013), by providing a pure physiological spectrum of ABP and HP fluctuations and by reducing the influence of non-baroreflex drives. Further studies are necessary for evaluation of this promising method for studying BRS and other CVS indexes during the dynamic processes of daily life. Atrial fibrillation (AF) presents NCVD events typically triggered by sympathovagal discharge (Goldstein, 2001), resulting in a dysfunctional atrial rhythm possessing as a consequence stroke, heart failure and risk of dementia. The electrical instability of atria, both focal and re-entrant activity, are progressive, self-feedback processes that evolve paroxysmal AF toward its persistent form. These classical experimental and clinical observations were without clear evidence-based pathophysiological explanation (Schotten et al., 2011). Ashton et al. propose the morpho-functional remodeling of ANS, both of its extrinsic (pre and post-ganglionic neurons) and intrinsic components (ganglionated plexus) that innervate the heart, as fundamentally contributing to positive feedback mechanism of AF. The ganglionated plexus, is the network of acetylcholine and other neurochemically distinct neurons, which play an important role in the modulation of cholinergic transmission and can be the site of maladaptive changes including arrhythmogenesis. These maladaptive changes could be based on both short-term and long-term plasticity mechanisms, with engagement of 5HT3 receptors, acetylcholine release and NO signaling in sympathetic neurons, and nicotinic expression, NO-cGMP signaling and NMDA receptor expression in vagal neurons. The neurochemical profile of synaptic plasticity of both sympathetic and vagal ganglionic transmission is promising target for future pharmacological studies aiming to intervene in AF that is morpho-functionally stabile as its persistent form. Traditional ECG and HRV linear and nonlinear indexes (Kikillus et al., 2007) could not give the answers about regulatory mechanisms of longer time scales and their differences between healthy and AF patients. Differences in nonlinear HP functional patterns between healthy and AF patients could be of major importance for the diagnosis and consequent therapy of AF forms that are of central neural origin (Andrade et al., 2014). This was the focus of the research of Platiša et al., where novel Generalized Poincaré Plot (GPP) analysis of RR interval was proposed as a sensible method for distinction of AF from healthy subjects. GPP revealed for the first-time different system dynamics for large time scales in AF and healthy subjects. In the special case when GPP analysis was performed between 100 preceding and 100 following RR intervals, distinct regimes could be observed in healthy subjects, reflecting hypothetical different set-points of the blood pressure-heart rate baroreflex loop. In AF patients the GPP profile of RR intervals were scattered. This result suggests that AF patients have smaller adaptive capacity to internal and external perturbations. Four cluster profile of correlation maxima and their absolute values for different correlation scales were also different in healthy and AF patients. These results supported the hypothesis that regulatory regimes in healthy subjects operate in fine tuned superimposed regimes acting on different time scales-parasympathetic, sympathetic and slow regulatory mechanisms like thermoregulation, rennin-angiotensin-aldosterone-sodium system, hormones etc. The AF cluster pattern was highly distorted, shifted toward higher frequencies and with increased randomness. The new GPP methodological approach for detection and profiling cardiovascular regimes need future pharmacological evaluation and potential translatory development as a diagnostic tool for AF and other NCVD. ANS is coupled directly to the cardiovascular system, but also through the interface of an energy regulating system. This is why an imbalance of energy regulation, as it is the case in obesity, often represents the first step, or the initial trigger of a neurally and metabolically mediated cascade of cardiovascular complications (hypertension, generalized atherosclerotic diathesis, dyslipidemia, diabetes mellitus type II). Digestion, absorption and neuroendocrine activity associated with the adoption of food has a direct effect on cardiovascular regulating centers (de Lartigue, 2014), pointing to the vagal subsystem as the potential target for neuromodulatory and pharmacological interventions in treatment of obesity, and, consequently, obesity related diseases. This important aspect of NCVD was reviewed by Guarino et al. Even though this comprehensive overview emphasizes the potential vagal route for neuromodulation and pharmacological intervention, the sympathetic route was also evaluated. This, more complex and differently structured subsystem presents itself as less understood and consequently is a significantly diminished path for obesity and related NCVD treatment. Its anatomical characteristics, i.e., approachability by external manipulations imply that the pharmacological approach should be investigated in the future, while the vagal route offers a good basis for both neuromodulatory and pharmacological strategies. Vagal modulation, in specific transcutaneous auricular vagus nerve stimulation is a promising method for body weight reduction in obese patients, also resulting with significant improvements of cardiometabolic profile. Sympathetic modulation, with inconsistent results on body weight reduction and partial cardiometabolic effects, from a results prospective, is a less promising strategy. Hyperadrenergic state is the classical hallmark of heart failure (HF), the clinical endpoint of the number of CVD (Marwick, 2018). Toschi-Dias et al. details elaborates the neurohumoral responses to hemodynamic stress, the common initial event of the HF hyperadrenergic state. The HF hyperadrenergic state, associated with different reductions of left ventricular ejection fraction (LVEF: preserved-p, mid-range-mr, reduced-r) is also associated with different morpho-functional remodeling of left ventricle, specific for its ejection functioning. This interesting association, widely recognized as a valuable prognostic and diagnostic parameter (Marwick, 2018) seeks for a deeper genetic and/or environmental influence studies, due to the hypothesis that different pathophysiological patterns might sculpture different EF phenotypes in HF. Vascular remodeling in HF further complicates an ANS functional profile, pushing it toward the maintenance and/or enhancement of hyperadrenergic state. Arterial baroreceptor (ABR) dysfunction, mostly due to the decrease of large vessel elasticity following chronic hypertension, distinguishes itself as an important factor in generation of cardiac diastolic dysfunction. ABR seem to play dominant role in sustaining hyperadrenergic state both in HFrEF (low stroke volume) and HFpEF (increased vascular stiffness), by different mechanisms. This issue necessitates future investigations of the hierarchical (in sense of absolute and relative quantitative contribution to the hyperadrenergic state) and temporal order of cardiovascular reflexes engaged in HFmrEF and HFpEF. It is reasonable to hypothesize that different quantitative and temporal patterns of cardiovascular reflex response result with different HFEF phenotypes. Cardiopulmonary reflex (CPR) regulates the state of systemic blood volume by (a) sympathetic modulation (low intensity changes), (b) release of atrial Na-uretic peptide, and (c) by strengthening and enhancing ABR action at high intensity changes. In HFrEF patients, no reduction of sympathetic outflow is obtained by CPR unloading. Participation of cardiac sympathetic afferent reflex and arterial chemoreflex was thoroughly evidenced in the hyperadrenergic state of HFrEF, while their role in HFpEF and HFmrEF needs future evaluation. A number of surgical and pharmacological interventions manifest NCV disturbances as an important caveat. For that reason, detailed and comprehensive NCV evaluation becomes a constituent part of pre-interventional evaluation of the patient and postintervention follow up. Coronary artery bypass graft (CABG) surgery can induce disbalance of sympathovagal ratio and, consequently respiratory depression, approximately 5 days after the surgery (Aronson et al., 2011; Pantoni et al., 2014; Patron et al., 2014; Ksela et al., 2015). In order to identify a prognostic marker, Costa et al. studied the prognostic significance of perioperative arterial blood pressure (ABP) variability for the occurrence of respiratory depression following CABG. The finding of Costa et al. that ABP variability parameters have prognostic value for respiratory depression has both pathophysiological and clinical significance. Deep brain stimulation (DBS) represents an invasive, frequent and developing intervention for the treatment of a spectrum of neurological diseases, with Parkinson's disease (PD) as the most common. As reported by Chowdhury et al., hemodynamic perturbations, like hypertension, hypotension, bradycardia, tachycardia and arrhythmia are frequent side-effects of this procedure in PD patients. They can be the consequence of (a) the independent or accompanying autonomic co-morbidity of the main PD pathological process, (b) the procedures associated to the surgery (semi-sitting position, anesthetics, sedation, stress, electrode battery placement and the stimulation of brain nuclei itself). Significant predictor potential has only pre-operative ABP, with diastolic BP as the marker most associated with hemodynamic event. Hypertension, predisposing factor of cerebral hemorrhage was noted during electrode placement and nuclei stimulation. A prospective study is needed for detailed hemodynamic evaluation (a) during the DBS surgery and the estimation of (b) pre- and (c) post-operative autonomic status of the PD patients. This approach would potentially change protocols of presurgical evaluation and postsurgical treatment of PD patients subjected to DBS. After heart transplantation, the autonomic reinnervation of the transplanted heart has important consequences on its reactivity and hemodynamical adaptability (exercise capacity, coronary blood flow regulation (Grupper et al., 2018). Wdowczyk et al. present a novel tool, Transition Networks, in the case report that has potential for distinguishing HRV increase due to reinnervation. Further stratified longitudinal clinical studies are needed for evaluation of this method. However, the capacity of the method to offer an insight into dynamical inter-beat dependences of RR intervals enounce better comprehension of the transplant functional adoption into CVS neural network. Pharmacological interventions in neuropsychiatric patients often disturb autonomic balance. Li et.al. applied their method (long-term Multiple Trigonometric Spectral Analysis, Li et al.) combined with conventional liner parameters of HRV as a tool for predicting fingolimod-induced bradycardia in patients with multiple sclerosis (MS). On the basis of their analysis they report an increased pre medication parasympathetic activity as the predisposing factor for fingolimod induced bradycardia, with pretreatment HR as the only predicting factor. Yuen et al. performed the meta-analysis on clozapine induced autonomic dysfunction in patients with schizophrenia. They conclude that the most frequent complications were myocarditis, orthostatic hypotension and tachycardia, prevalently due to sympathetic overactivity. This report emphasizes the need for introduction of post-medication NCV evaluation autonomic tests for prevention and therapeutic coping with clozapine-induced side-effects. In accordance with Li et al., an intuitive direction for future investigations would be the identification of NCV predicting parameters for clozapine induced autonomic side effects. Cerebral blood flow (CBF) is an issue of the upmost importance for understanding the NCVD, from two aspects: a. A growing corpus of data supports the standpoint that besides the cerebral autoregulation (Silvani et al., 2004; Zoccoli et al., 2005), sympathetic (Cassaglia et al., 2008; ter Laan et al., 2013; Frederiksen et al., 2017), parasympathetic (Purkayastha et al., 2018) and sensory innervations (Branston et al., 1995) functionally participate in the regulation of CBF. b. Compromise of CBF, especially in the neonates, can cause serious, life threatening autonomic dysfunctions (Silvani et al., 2004; Metzler et al., 2017; Campbell et al., 2018). Glutamate is considered to be the neurochemical initiator and executor of brain injury in hypoxic-ischemic brain disorder (HIBD). Dang et al. report “two phase” change of basal ganglia glutamate level after HIBD, that is significantly and negatively correlated to the brain perfusion fraction. Even though this association is suggestive for negative electrochemical coupling of cerebral activity (glutamate) and brain perfusion in HIBD, further investigations are necessary for elucidating the mechanism(s) of brain activity-blood flow coupling in HIBD. Special emphasis should be on the role of sympathetic nervous system in cerebral activity-blood flow regulation in HIBD (Ainslie, 2008; Cassaglia et al., 2008; Edvinsson, 2008; Immink and Passier, 2008; Levine and Zhang, 2008; Ogoh, 2008; Paulson and Knudsen, 2008; Prakash, 2008; Visocchi, 2008; Yildiz, 2008; ter Laan et al., 2013). An important protective effect of artesunate against necrosis in cerebral infarction was reported by Shao et al., suggesting an autophagy as the most probable mechanism. Potential treatment by artesunate could have beneficiary effect for autonomic dysfunction, an important caveat of neonatal hypoxic-ischemic encephalopathy (Metzler et al., 2017; Campbell et al., 2018). In conclusion, the presented physiological, methodological and pathophysiological aspects of NCVD point to the importance of consideration of NCVD from integrative point of view and as a constitutive part of different pathophysiological entities. Application of novel mathematical methods for molecular targeting and systemic characterizing of NCVD enounce promising lines of future research in the translational science. Neurocardiovascular side-effects of surgical and pharmacological interventions emphasize the importance of ANS evaluation before and after the intervention as the routine procedure in the clinical work. Finally, an intriguing role of autonomic networks, traditionally considered the cardiovascular neural subsystem, in the regulation of cerebral blood flow both in physiological and pathophysiological conditions is about to open a novel aspect of cerebro-cardiovascular integration. The author confirms being the sole contributor of this work and has approved it for publication. This work was supported by Ministry of Education, Science and Technological Development of the Republic of Serbia, grant number III 41028. The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Acevedo, E. O., Webb, H. E., Weldy, M. L., Fabianke, E. C., Orndorff, G. R., and Starks, M. A. (2006). Cardiorespiratory responses of Hi Fit and Low Fit subjects to mental challenge during exercise. Int. J. Sports Med. 27, 1013–1022. doi: 10.1055/s-2006-923902 PubMed Abstract | CrossRef Full Text | Google Scholar Ainslie, P. N. (2008). Comments on Point: Counterpoint: Sympathetic activity does/does not influence cerebral blood flow. J. Appl. Physiol. (1985) 105:1370. doi: 10.1152/japplphysiol.90597.2008a CrossRef Full Text | Google Scholar Allman, J., Hakeem, A., and Watson, K. (2002). Two phylogenetic specializations in the human brain. Neuroscientist 8, 335–346. doi: 10.1177/107385840200800409 PubMed Abstract | CrossRef Full Text | Google Scholar Andrade, J., Khairy, P., Dobrev, D., and Nattel, S. (2014). The clinical profile and pathophysiology of atrial fibrillation: relationships among clinical features, epidemiology, and mechanisms. Circ. Res. 114, 1453–1468. doi: 10.1161/CIRCRESAHA.114.303211 PubMed Abstract | CrossRef Full Text Aronson, S., Dyke, C. M., Levy, J. H., Cheung, A. T., Lumb, P. D., Avery, E. G., et al. (2011). Does perioperative systolic blood pressure variability predict mortality after cardiac surgery? An exploratory analysis of the ECLIPSE trials. Anesth. Analg. 113, 19–30. doi: 10.1213/ANE.0b013e31820f9231 PubMed Abstract | CrossRef Full Text | Google Scholar Bajić, D., Lončar-Turukalo, T., Stojičić, S., Šarenac, O., Bojić, T., Murphy, D., et al. (2010). Temporal analysis of the spontaneous baroreceptor reflex during mild emotional stress in the rat. Stress 13, 142–154. doi: 10.3109/10253890903089842 PubMed Abstract | CrossRef Full Text | Google Scholar Bojić, T. (2003). Mechanisms of Neural Control and Effects of Acoustic Stimulation on Cardiovascular System During the Wake-Sleep Cycle. Ph.D. Experimental, Alma Mater Università di Bologna. Branston, N. M., Umemura, A., and Koshy, A. (1995). Contribution of cerebrovascular parasympathetic and sensory innervation to the short-term control of blood flow in rat cerebral cortex. J. Cereb. Blood Flow Metab. 15, 525–531. doi: 10.1038/jcbfm.1995.65 PubMed Abstract | CrossRef Full Text | Google Scholar Campbell, H., Govindan, R. B., Kota, S., Al-Shargabi, T., Metzler, M., Andescavage, N., et al. (2018). Autonomic dysfunction in neonates with hypoxic ischemic encephalopathy undergoing therapeutic hypothermia impairs physiological responses to routine care events. J. Pediatr. 196, 38–44. doi: 10.1016/j.jpeds.2017.12.071 PubMed Abstract | CrossRef Full Text | Google Scholar Cassaglia, P. A., Griffiths, R. I., and Walker, A. M. (2008). Sympathetic withdrawal augments cerebral blood flow during acute hypercapnia in sleeping lambs. Sleep 31, 1729–1734. doi: 10.1093/sleep/31.12.1729 PubMed Abstract | CrossRef Full Text | Google Scholar Coleman, T. G. (1980). Arterial baroreflex control of heart rate in the conscious rat. Am. J. Physiol. 238, H515–520. doi: 10.1152/ajpheart.1980.238.4.H515 PubMed Abstract | CrossRef Full Text | Google Scholar de Lartigue, G. (2014). Putative roles of neuropeptides in vagal afferent signaling. Physiol. Behav. 136, 155–169. doi: 10.1016/j.physbeh.2014.03.011 PubMed Abstract | CrossRef Full Text | Google Scholar Doliana, R., Veljković, V., Prljić, J., Veljković, N., De Lorenzo, E., Mongiat, M., et al. (2008). EMILINs interact with anthrax protective antigen and inhibit toxin action in vitro. Matrix Biol. 27, 96–106. doi: 10.1016/j.matbio.2007.09.008 PubMed Abstract | CrossRef Full Text | Google Scholar Du, D., Hu, L., Wu, J., Wu, Q., Cheng, W., Guo, Y., et al. (2017). Neuroinflammation contributes to autophagy flux blockage in the neurons of rostral ventrolateral medulla in stress-induced hypertension rats. J. Neuroinflammation 14:169. doi: 10.1186/s12974-017-0942-2 PubMed Abstract | CrossRef Full Text | Google Scholar Edvinsson, L. (2008). Comments on point:counterpoint: sympathetic activity does/does not influence cerebral blood flow. sympathetic nerves influence the cerebral circulation. J. Appl. Physiol. (1985) 105, 1370–1371. PubMed Abstract | Google Scholar Ernsting, J., and Parry, D. J. (1957). Some observations on the effect of stimulating the strech receptors in the carotid artery of men. J. Physiol. (Lond). 137, 45–46. Frederiksen, S. D., Haanes, K. A., Warfvinge, K., and Edvinsson, L. (2017). Perivascular neurotransmitters: regulation of cerebral blood flow and role in primary headaches. J. Cereb. Blood Flow Metab. 1:271678X17747188. doi: 10.1177/0271678X17747188 CrossRef Full Text | Google Scholar Fritsch, J. M., Eckberg, D. L., Graves, L. D., and Wallin, B. G. (1986). Arterial pressure ramps provoke linear increases of heart period in humans. Am. J. Physiol. 251(6 Pt 2), R1086–R1090. doi: 10.1152/ajpregu.1986.251.6.R1086 PubMed Abstract | CrossRef Full Text | Google Scholar Glišić, S., Cavanaugh, D. P., Chittur, K. K., Senćanski, M., Perović, V., and Bojić, T. (2016). Common molecular mechanism of the hepatic lesion and the cardiac parasympathetic regulation in chronic hepatitis C infection: a critical role for the muscarinic receptor type 3. BMC Bioinformatics 17:139. doi: 10.1186/s12859-016-0988-7 PubMed Abstract | CrossRef Full Text | Google Scholar Godoy, L. D., Rossignoli, M. T., Delfino-Pereira, P., Garcia-Cairasco, N., and de Lima Umeoka, E. H. (2018). A comprehensive overview on stress neurobiology: basic concepts and clinical implications. Front. Behav. Neurosci. 12:127. doi: 10.3389/fnbeh.2018.00127 PubMed Abstract | CrossRef Full Text | Google Scholar Goldstein, D. (2001). The Autonomic Nervous System in Health and Disease. 1st Edn. New York, NY: Marcel Dekker, Inc. Grupper, A., Gewirtz, H., and Kushwaha, S. (2018). Reinnervation post-heart transplantation. Eur. Heart J. 39, 1799–1806. doi: 10.1093/eurheartj/ehw604 PubMed Abstract | CrossRef Full Text | Google Scholar Immink, R. V., and Passier, R. H. (2008). Comments on point:counterpoint: sympathetic activity does/does not influence cerebral blood flow. the sympathetic “knock-out” model. J. Appl. Physiol. (1985) 105, 1372–1373. Google Scholar Investigators, W. M. P. P. (1988). The World Health Organization MONICA Project (monitoring trends and determinants in cardiovascular disease): a major international collaboration. J. Clin. Epidemiol. 41, 105–114. doi: 10.1016/0895-4356(88)90084-4 CrossRef Full Text | Google Scholar Kapidžić, A., Platiša, M. M., Bojić, T., and Kalauzi, A. (2014). RR interval-respiratory signal waveform modeling in human slow paced and spontaneous breathing. Respir. Physiol. Neurobiol. 203, 51–59. doi: 10.1016/j.resp.2014.08.004 PubMed Abstract | CrossRef Full Text | Google Scholar Kikillus, N., Hammer, G., Wieland, S., and Bolz, A. (2007). Algorithm for identifying patients with paroxysmal atrial fibrillation without appearance on the ECG. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2007, 275–278. doi: 10.1109/IEMBS.2007.4352277 PubMed Abstract | CrossRef Full Text | Google Scholar Kirchheim, H. R. (1976). Systemic arterial baroreceptor reflexes. Physiol. Rev. 56, 100–177. doi: 10.1152/physrev.1976.56.1.100 PubMed Abstract | CrossRef Full Text | Google Scholar Ksela, J., Avbelj, V., and Kalisnik, J. M. (2015). Multifractality in heartbeat dynamics in patients undergoing beating-heart myocardial revascularization. Comput. Biol. Med. 60, 66–73. doi: 10.1016/j.compbiomed.2015.02.012 PubMed Abstract | CrossRef Full Text | Google Scholar La Rovere, M. T., Bigger, J. T. Jr., Marcus, F. I., Mortara, A., and Schwartz, P. J. (1998). Baroreflex sensitivity and heart-rate variability in prediction of total cardiac mortality after myocardial infarction. ATRAMI (Autonomic Tone and Reflexes After Myocardial Infarction) Investigators. Lancet 351, 478–484. doi: 10.1016/S0140-6736(97)11144-8 PubMed Abstract | CrossRef Full Text | Google Scholar Lamberti, J. J. Jr., Urquhart, J., and Siewers, R. D. (1968). Observations on the regulation of arterial blood pressure in unanesthetized dogs. Circ. Res. 23, 415–428. doi: 10.1161/01.RES.23.3.415 PubMed Abstract | CrossRef Full Text Levine, B. D., and Zhang, R. (2008). Comments on point:counterpoint: sympathetic activity does/does not influence cerebral blood flow. Autonomic control of the cerebral circulation is most important for dynamic cerebral autoregulation. J. Appl. Physiol. (1985) 105, 1369–1373. doi: 10.1152/japplphysiol.zdg-8199.pcpcomm.2008 CrossRef Full Text | Google Scholar Libbus, I., Nearing, B. D., Amurthur, B., KenKnight, B. H., and Verrier, R. L. (2016). Autonomic regulation therapy suppresses quantitative T-wave alternans and improves baroreflex sensitivity in patients with heart failure enrolled in the ANTHEM-HF study. Heart Rhythm 13, 721–728. doi: 10.1016/j.hrthm.2015.11.030 PubMed Abstract | CrossRef Full Text | Google Scholar Mancia, G., and Mark, A. L. (1983). “Arterial baroreflex in humans,” in Handbook of Physiology, Section 2: The Cardiovascular System, eds J. T. Shepherd and F. M. Abboud (Bethesda, MD: American Physiological Society), 755–793. Marwick, T. H. (2018). Ejection fraction pros and cons: JACC state-of-the-art review. J. Am. Coll. Cardiol. 72, 2360–2379. doi: 10.1016/j.jacc.2018.08.2162 PubMed Abstract | CrossRef Full Text | Google Scholar Metzler, M., Govindan, R., Al-Shargabi, T., Vezina, G., Andescavage, N., Wang, Y., et al. (2017). Pattern of brain injury and depressed heart rate variability in newborns with hypoxic ischemic encephalopathy. Pediatr. Res. 82, 438–443. doi: 10.1038/pr.2017.94 PubMed Abstract | CrossRef Full Text | Google Scholar Ogoh, S. (2008). Comments on Point:Counterpoint: Sympathetic activity does/does not influence cerebral blood flow. Autonomic nervous system influences dynamic cerebral blood flow. J. Appl. Physiol. (1985) 105:1370. Google Scholar Oosting, J., Struijker-Boudier, H. A., and Janssen, B. J. (1997). Validation of a continuous baroreceptor reflex sensitivity index calculated from spontaneous fluctuations of blood pressure and pulse interval in rats. J. Hypertens. 15, 391–399. doi: 10.1097/00004872-199715040-00010 PubMed Abstract | CrossRef Full Text | Google Scholar Pantoni, C. B., Mendes, R. G., Di Thommazo-Luporini, L., Simoes, R. P., Amaral-Neto, O., Arena, R., et al. (2014). Recovery of linear and nonlinear heart rate dynamics after coronary artery bypass grafting surgery. Clin. Physiol. Funct. Imaging 34, 449–456. doi: 10.1111/cpf.12115 PubMed Abstract | CrossRef Full Text | Google Scholar Parati, G., Di Rienzo, M., Castiglioni, P., Bouhaddi, M., Cerutti, C., Cividjian, A., et al. (2004). Assessing the sensitivity of spontaneous baroreflex control of the heart: deeper insight into complex physiology. Hypertension 43, e32–e34; author reply e32-34. doi: 10.1161/01.HYP.0000126689.12940.cd PubMed Abstract | CrossRef Full Text Patron, E., Messerotti Benvenuti, S., and Palomba, D. (2014). Preoperative and perioperative predictors of reactive and persistent depression after cardiac surgery: a three-month follow-up study. Psychosomatics 55, 261–271. doi: 10.1016/j.psym.2013.12.011 PubMed Abstract | CrossRef Full Text | Google Scholar Paulson, O. B., and Knudsen, G. M. (2008). Comments on point:counterpoint: sympathetic activity does/does not influence cerebral blood flow. Role of a rudimentary sympathetic nervous system on cerebral blood flow. J. Appl. Physiol. (1985) 105, 1371–1372. Google Scholar Perović, V. R., Muller, C. P., Niman, H. L., Veljković, N., Dietrich, U., Tošić, D. D., et al. (2013). Novel phylogenetic algorithm to monitor human tropism in Egyptian H5N1-HPAIV reveals evolution toward efficient human-to-human transmission. PLoS ONE 8:e61572. doi: 10.1371/journal.pone.0061572 PubMed Abstract | CrossRef Full Text | Google Scholar Pierce, M. L., Deuchars, J., and Deuchars, S. A. (2010). Spontaneous rhythmogenic capabilities of sympathetic neuronal assemblies in the rat spinal cord slice. Neuroscience 170, 827–838. doi: 10.1016/j.neuroscience.2010.07.007 PubMed Abstract | CrossRef Full Text | Google Scholar Prakash, E. S. (2008). Comments on Point:Counterpoint: Sympathetic activity does/does not influence cerebral blood flow. When noradrenergic restraint of cerebral blood flow makes homeostatic sense. J. Appl. Physiol. (1985) 105:1373. Google Scholar Purkayastha, S., Maffuid, K., Zhu, X., Zhang, R., and Raven, P. B. (2018). The influence of the carotid baroreflex on dynamic regulation of cerebral blood flow and cerebral tissue oxygenation in humans at rest and during exercise. Eur. J. Appl. Physiol. 118, 959–969. doi: 10.1007/s00421-018-3831-1 PubMed Abstract | CrossRef Full Text | Google Scholar Schotten, U., Verheule, S., Kirchhof, P., and Goette, A. (2011). Pathophysiological mechanisms of atrial fibrillation: a translational appraisal. Physiol. Rev. 91, 265–325. doi: 10.1152/physrev.00031.2009 PubMed Abstract | CrossRef Full Text | Google Scholar Silvani, A., Asti, V., Bojić, T., Ferrari, V., Franzini, C., Lenzi, P., et al. (2005). Sleep-dependent changes in the coupling between heart period and arterial pressure in newborn lambs. Pediatr. Res. 57, 108–114. doi: 10.1203/01.PDR.0000148065.32413.B0 PubMed Abstract | CrossRef Full Text | Google Scholar Silvani, A., Bojić, T., Cianci, T., Franzini, C., Lodi, C. A., Predieri, S., et al. (2003). Effects of acoustic stimulation on cardiovascular regulation during sleep. Sleep 26, 201–205. doi: 10.1093/sleep/26.2.201 PubMed Abstract | CrossRef Full Text | Google Scholar Silvani, A., Bojić, T., Franzini, C., Lenzi, P., Walker, A. M., Grant, D. A., et al. (2004). Sleep-related changes in the regulation of cerebral blood flow in newborn lambs. Sleep 27, 36–41. doi: 10.1093/sleep/27.1.36 PubMed Abstract | CrossRef Full Text | Google Scholar Strickland, O. L., Giger, J. N., Nelson, M. A., and Davis, C. M. (2007). The relationships among stress, coping, social support, and weight class in premenopausal African American women at risk for coronary heart disease. J. Cardiovasc. Nurs. 22, 272–278. doi: 10.1097/01.JCN.0000278964.05748.d8 PubMed Abstract | CrossRef Full Text | Google Scholar Subha, M., Pal, P., Pal, G. K., Habeebullah, S., Adithan, C., and Sridhar, M. G. (2016). Decreased baroreflex sensitivity is linked to sympathovagal imbalance, low-grade inflammation, and oxidative stress in pregnancy-induced hypertension. Clin. Exp. Hypertens. 38, 666–672. doi: 10.1080/10641963.2016.1200596 PubMed Abstract | CrossRef Full Text | Google Scholar ter Laan, M., van Dijk, J. M., Elting, J. W., Staal, M. J., and Absalom, A. R. (2013). Sympathetic regulation of cerebral blood flow in humans: a review. Br. J. Anaesth. 111, 361–367. doi: 10.1093/bja/aet122 PubMed Abstract | CrossRef Full Text | Google Scholar Ulrich-Lai, Y. M., and Herman, J. P. (2009). Neural regulation of endocrine and autonomic stress responses. Nat. Rev. Neurosci. 10, 397–409. doi: 10.1038/nrn2647 PubMed Abstract | CrossRef Full Text | Google Scholar Veljković, N., Glišić, S., Perović, V. R., and Veljković, V. (2011). The role of long-range intermolecular interactions in discovery of new drugs. Expert Opin. Drug Discov. 6, 1263–1270. doi: 10.1517/17460441.2012.638280 PubMed Abstract | CrossRef Full Text | Google Scholar Veljković, V., Cosić, I., Dimitrijević, B., and Lalović, D. (1985). Is it possible to analyze DNA and protein sequences by the methods of digital signal processing? IEEE Trans. Biomed. Eng. 32, 337–341. doi: 10.1109/TBME.1985.325549 PubMed Abstract | CrossRef Full Text | Google Scholar Veljković, V., Mouscadet, J. F., Veljković, N., Glišić, S., and Debyser, Z. (2007). Simple criterion for selection of flavonoid compounds with anti-HIV activity. Bioorg. Med. Chem. Lett. 17, 1226–1232. doi: 10.1016/j.bmcl.2006.12.029 PubMed Abstract | CrossRef Full Text | Google Scholar Visocchi, M. (2008). Comments on point:counterpoint: sympathetic activity does/does not influence cerebral blood flow. sympathetic activity does influence cerebral blood flow. J. Appl. Physiol. (1985) 105:1369. Google Scholar Webb, H. E., Rosalky, D. A., McAllister, M. J., Acevedo, E. O., and Kamimori, G. H. (2017). Aerobic fitness impacts sympathoadrenal axis responses to concurrent challenges. Eur. J. Appl. Physiol. 117, 301–313. doi: 10.1007/s00421-016-3519-3 PubMed Abstract | CrossRef Full Text | Google Scholar Yildiz, M. (2008). Comments on point:counterpoint: sympathetic activity does/does not influence cerebral blood flow. J. Appl. Physiol. (1985) 105:1371. Google Scholar Ziemssen, T., Reimann, M., Gasch, J., and Rudiger, H. (2013). Trigonometric regressive spectral analysis: an innovative tool for evaluating the autonomic nervous system. J. Neural. Transm. 120 (Suppl. 1), S27–S33. doi: 10.1007/s00702-013-1054-5 PubMed Abstract | CrossRef Full Text | Google Scholar Zimmerman, M. C. (2011). Angiotensin II and angiotensin-1-7 redox signaling in the central nervous system. Curr. Opin. Pharmacol. 11, 138–143. doi: 10.1016/j.coph.2011.01.001 PubMed Abstract | CrossRef Full Text | Google Scholar Zoccoli, G., Bojić, T., and Franzini, C. (2005). “Regulation of cerebral circulation during sleep,” in The Physiological Nature of Sleep, 1st Edn, eds P. L. Parmeggiani and R. Velluti (London: Imperial College Press), 351–369. doi: 10.1142/9781860947186_0016 CrossRef Full Text | Google Scholar Keywords: neurocardiovascular diseases, Integrative pathology, autonomic nervous system, sympathetic, parasympathetic, development Citation: Bojić T (2019) Editorial: Neurocardiovascular Diseases: New Aspects of the Old Issues. Front. Neurosci. 12:1032. doi: 10.3389/fnins.2018.01032 Received: 18 October 2018; Accepted: 20 December 2018; Published: 11 January 2019. Edited and reviewed by: Vaughan G. Macefield, Baker Heart and Diabetes Institute, Australia Copyright © 2019 Bojić. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Tijana Bojić, [email protected]; [email protected]
Journal of Communications and Networks, Volume 20, pp 1-1; https://doi.org/10.1109/jcn.2018.000065

Abstract:
Presents the aim and scope of this publication. Also includes information for authors.
Tanya Zilberter, Yuri Zilberter
Published: 30 August 2018
Frontiers in Nutrition, Volume 5; https://doi.org/10.3389/fnut.2018.00075

Abstract:
Thomas Seyfried remarked in his book [(1), page 6]: “The definition of ketogenic diet allows for considerable leeway in food choices as long as the individual has reduced blood glucose and is producing ketones.” Unfortunately, these parameters are lacking in many if not most of studies into metabolic effects of macronutrients. Meanwhile, there is a precise way to predict whether or not a diet will induce ketosis and the aim of this opinion article is to advocate a broader usage of this way. Why is this so important? Excess of carbohydrate intake typical for consumers of the Western diet may cause detrimental effects on metabolism and increase risks of the onset and progression of many neurodegenerative diseases (2–4). On the other hand, diets high in fat and low in carbohydrates decrease appetite, probabilities of food addiction and obesity, and are neuroprotective (5, 6). Carbohydrate restriction induces physiological changes which are very similar to the well documented beneficial effects of calorie restriction (7, 8). Conversely, the hallmark of high-carbohydrate diets is homeostatic inadequacy (9), an overproduction of reactive oxygen species and advanced glycation products, both of which are implicated in neuroinflammation and neurodegeneration (10–12). However, the meaning of “high” or “low” in diets' definition has been drifting away from the previously established quantitative criterion known as ketogenic ratio. Almost a century ago, Woodyatt (13) wrote: “antiketogenesis is an effect due to certain products which occur in the oxidation of glucose, an interaction between these products on the one hand and one or more of the acetone bodies on the other.” The ketogenic ratio (KR), as proposed by Shaffer (14), is a ratio of the sum of ketogenic factors to the sum of antiketogenic factors: KR = K/AK. The antiketogenic part of the equation invariably equals 1 so the KRs are always expressed as 2:1, 4:1, etc. For the sake of economy of reading, we leaved out the repeating part not bearing any information and mention only the informative digit. Shaffer concluded that the maximal ratio compatible with the oxidation of the “ketogenic” molecules becomes possible at the KR = 1, making KRs below 1 antiketogenic and KRs above 2 ketogenic. Wilder and Winter (15) described the KR of a food in terms of times the fat content exceeds the amount of carbohydrate and protein combined, roughly. The reasoning was based on their own experimental observation that fats are predominantly ketogenic (90%), carbohydrates are almost 100% anti-ketogenic, and protein is both ketogenic and antiketogenic, 46–58% respectively. They arrived, along with Woodyatt and Sansum (13), at the conclusion that KR for induction of ketogenesis should be 2 or higher while the upper limit of antiketogenesis is 1. In 1980, Withrow (16) modified the equation and since that time, the equation looked like this: KR = (0.9 F + 0.46 P): (C + 0.58 P + 0.1 F) where F is grams of fat; P is grams of protein and C is grams of carbohydrate. Currently, this equation is rarely used in nutrition research and less so in dietetic practice, which is regrettable since properly calculated KRs reveals interesting patterns of diet effects. Previously (9), using the Withrow's equation, we calculated KRs in a number of diets and came to conclusion that the watershed in the group of effects occurs at KR of about 1.7. Above this value, metabolic features of diets were characteristic for ketogenesis, while below this value, they were characteristic for the obesogenic high-fat diet (oHFD), which, in contrast to the diet resulting in ketosis (KD), is high in fat but also in carbohydrates. Here, we analyzed three groups of diets in order to compare our observation regarding the watershed with the classification of diets made by the authors of 62 studies, in which it was possible to calculate KRs. We can see that there is no common criteria in choosing diet compositions for the “normal control” group to start with (Figure 1A). The vast majority of “normal control” diets are clearly anti-ketogenic (KRs below or equal 1). The oHFD group of diets had broader spectrum of KRs ranging from the anti-ketogenic 0.456 to clearly ketogenic 2.994. The KRs of diets, which were considered ketogenic by the authors, ranged especially broadly: from 0.36 to above 6. The macronutrient compositions of oHFD diets and KDs overlap although the obesogenic oHFD is discussed in literature as diametrically different from the KD. The metabo- and neuroprotective effects of KD are experimentally and clinically confirmed, however, the low compliance rate of the strict KD caused mass attempts to reduce the KR below KR = 2, which for a century used to be the minimal accepted value to consider a diet ketogenic. Figure 1. (A) Green line, theoretical threshold of ketogenesis; dashed green line, empirical threshold of ketogenesis. Red line, theoretical threshold of anti-ketogenesis; dashed red line KR = 0.5. Vertical axis, ketogenic ratios. Horizontal axix, original studies, first author and year (17–50). (B) Ketogenic ratios of diets below the ketogenic threshold. Calculated basing on data extracting from the studies (25, 51–54). Gray curves in (B,C) trendlines. (C) Body-mass loss on diets below the ketogenic threshold. VLCD, very low carbohydrate diet; MAD, modified Atkins diet; Zone, the Zone diet; KLC, ketogenic low carbohydrate diet; WW, Weight Watchers diet; VLFD, very low fat diet; NLC, non-ketogenic low carbohydrate diet. An example is the Modified Atkins Diet (MAD) first tried at The Johns Hopkins Hospital. It is a protocol replicating the induction phase of the original Atkins diet. MAD is composed of approximately 10% energy from carbohydrates, 30% from protein, and 60% from fat, no calorie restriction (55–58). The MAD became an intervention for treating a number of diseases, first of all in cases of intractable childhood epilepsy but also in pharmacoresistant epilepsy in adults and in pathologies of glucose utilization (58). MAD is labeled “ketogenic” while its KR = 1.3 (calculated basing on 11). However, its efficiency is limited: although 70% of epilepsy patients on MAD experienced a 50% reduction in seizures, after switching to the strict clinical KD (KR = 4) the patients benefited from an additional 37% improvement and 18.5% became seizure-free (13, 59, 60) indicating that the KR is indeed an important predictor of diet effectiveness. Basing on our review of KR-dependent effects of diets (6), here we compiled a brief overview in order to demonstrate the critical differences between KD and oHFD. 1. The risks of pathologies caused by brain hypometabolism (e.g., due to hypoxia, hypoglycemia, brain trauma) is reduced in diets capable of inducing ketogenesis; the opposite is shown for the oHFD diets (39, 61–69). 2. Inflammation, e.g., neuroinflammation is shown to be induced by oHFD but decreased by KD (70–78) among other things resulting in improved or impaired cognitive function (76, 78–82). 3. Neuronal hyperactivity and epilepsy is attenuated by KD but exacerbated by oHFD (75, 83–85, 85–88). 4. Inhibition of growth of tumor and metastasis as well as tumor neo-angiogenesis is demonstrated for KD while oHFD increases the risk of cancerogenesis (8, 89–92). 5. KD decreased cardiovascular risks while oHFD increases them (93, 94). 6. KD lowered the type 2 diabetes risks, improves management of complications and glucose control while oHFD increases the risks, exacerbates complications and induced glucose intolerance (95–99). In the ragne of KRs between 1 and 2 lays the area of metabolic uncertainty where the effects are poorly predictable, the definitions are vague and outcomes even more so. The most critical value in this area is 1.5, the experimentally reached threshold of anti-ketogenesis (100). Here we report the result of our analysis of non-ketogenic diets (Figures 1B,C) using data extracted from the studies: • Very low carbohydrate diet [VLCD, (51)]; • The Atkins diet - induction and maintaining phases, the Zone diet and very low fat diet, [VLFD, (52, 53)]; • MAD, Zone, Weight Watchers diet (WW) and VLFD (54); • Ketogenic low carbohydrate diet (KLC) and non-ketogenic low carbohydrate diet (NLC) (25). The common feature of these diets was that none of them reached the threshold of ketogenesis defined as KR = 1.5 (100). The diets were roughly isocaloric (1,412 ± 35.5 Kcal/day; caloric intake of WW varied averaging 1,400 Kcal), with main outcome a body-mass loss. Beyond the strictly utilitarian standpoint, body-mass loss is an indicator of lipolysis and thus of ketogenesis likelihood. In diets ranging from KR = 1.413 to KR = 0.06 (Figure 1B), the metabolic outcome did not depend on KR as directly as it does above the ketogenic threshold (Figure 1C) indicating that mechanisms other than the ketogenesis-glycolysis counterbalancing seem to be predominant. Indeed, in the study of Johnston et al. (25) the inverse relationship has been observed: the KLC diet having six times higher KR than NLC (0.35 vs. 0.06) had almost twice lower effect (4 vs. 7 kg) leading the authors to conclusion they even used as the article title: “Ketogenic low-carbohydrate diets have no metabolic advantage over nonketogenic low-carbohydrate diets”—although both diets were undoubtedly anti-ketogenic. It has been shown before (26) that the prevalence of carbohydrates in an otherwise equally high-protein diets increased energy intake in the ad libitum consumption mode initiating the vicious cycle of non-homeostatic processes, including reward seeking and food addiction (101) and the prevalence of energy-conserving metabolic mode over the homeostatically balanced mode (9). Theoretically, below the KR = 1.5, glucocentric metabolic mode prevails while above KR = 1.5 the dominant metabolic mode is adipocentric with the important consequence being initiation of lipolysis (102). This is why the body-mass loss of diets is a convenient parameter indirectly indicating that lipolytic processes take place. It has been measured empirically (100) that ketosis is not observed in KRs below 1.5—however, the “ketogenic” label has been assigned to MAD (KR = 1.021) and even to KLC (KR = 0.36). There is a turning point in the KR-effect interaction curve at KR = 0.5 (Figure 1C), the diet nearest to this point being at KR = 0.438 [the Zone diet, (52–54)]. Further decrease of KRs up to the value of 0.06 resulted in an inverted U-shaped dose-response relationship under the threshold of ketogenesis. Currently the classification of diets is rather unsatisfactory and diet labels offered by the authors (quite arbitrarily) “normal”, “oHFD,” or “ketogenic” oftentimes do not correspond to their respective diets' macronutrient compositions. Description based on percentages of energy from each of the macronutrients does not make it easy to qualify diet type and unify the categorization. The macronutrient ratio in terms of ketogenicity is often ignored in qualification of metabolic effects. The most striking example of this is the oHFD diet, which in fact is also high in carbohydrates. As we briefly discussed above, its effects are diametrically opposing those of the ketogenic diet which is also high in fat but low in carbohydrates, resulting in striking differences in diets' physiological effects (see Effects of KD vs. oHFD). The ad libitum access to food is the standard protocol in animal experiments although the validity of it is rightfully questioned since the subjects become “sedentary, obese, glucose intolerant, and on a trajectory to premature death” [(103), page 6,127]. On the other hand, the low-carbohydrate diets that are high in fat have a number of metabolic advantages: for instance, they facilitate increase energy expenditure by increasing thermogenic effects and excretion of ketone bodies (104). Greater carbohydrate intake was associated with poorer performance in patients with Alzheimer's disease (105), while KD improved cognition independent of weight loss in healthy human subjects (80). KD improved verbal vocabulary and reaction time in children with epilepsy and attention (4), concentration, and memory in adults with multiple sclerosis (106). Diets limiting carbohydrate intake mimic the effects of fasting or caloric restriction (102). In fact, calorie restriction is not even required on a very low carbohydrate diet to achieve the desired goals, while on a low-fat, high-carbohydrate diet calorie restriction is the principal requirement (107). The metabolic effects of dietary fat on energy homeostasis differs from the effects of carbohydrates in two key features. One is the ability to store energy in depots - fat is exceptionally good at it, but carbohydrates are limited in this ability. The other is the ability to increase the drive to consume energy. Carbohydrates have a characteristic ability to elicit positive reward and thus addiction (5, 101, 108–110) while significant carbohydrate restriction in VLCD caused not only energy intake decrease but also energy expenditure increase in both resting and active states (51). In spite of these non-homeostatic features, these mechanisms are evolutionarily appropriate in wild nature, but as soon as the living conditions change the hard-wired pursuit to maximize the energy store becomes a metabolic trap (9s), resulting in non-homeostatic overconsumption and all the negative metabolic consequences it causes. To conclude, the current classification of diets results in terminological confusion. We suggest that rethinking the existing descriptive approach and reanimating the century-old qualitative and clear-cut criterion may facilitate the use of common language and substantive discussion in nutrition and metabolism. TZ and YZ equally contributed to the concept, data collection and analysis, writing the manuscript and preparing illustrations. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 1. Seyfried TN. Cancer as a Metabolic Disease: On the Origin, Management, and Prevention of Cancer. Hoboken, NJ: Wiley (2012) 2. Seneff S, Wainwright G, Mascitelli L. Nutrition and Alzheimer's disease: the detrimental role of a high carbohydrate diet. Eur J Intern Med. (2011) 22:134–40. doi: 10.1016/j.ejim.2010.12.017 PubMed Abstract | CrossRef Full Text | Google Scholar 3. Miranda HV, Outeiro TF. The sour side of neurodegenerative disorders: the effects of protein glycation. J Pathol. (2010) 221:13–25. doi: 10.1002/path.2682 CrossRef Full Text | Google Scholar 4. Auburger G, Kurz A. The role of glyoxalases for sugar stress and aging, with relevance for dyskinesia, anxiety, dementia and Parkinson's disease. Aging (2011) 3:5–9. doi: 10.18632/aging.100258 PubMed Abstract | CrossRef Full Text | Google Scholar 5. Avena NM, Gold MS. Food and addiction – sugars, fats and hedonic overeating. Addiction (2011) 106:1214–5. discussion: 1219–20. doi: 10.1111/j.1360-0443.2011.03373.x PubMed Abstract | CrossRef Full Text | Google Scholar 6. Zilberter T, Zilberter Y. Energy metabolism: from neurons and glia to the whole brain: pathology and metabolic correction. Adv Physiol Sci. (2012) 43:37–54. 7. Domouzoglou E, Maratos-Flier E. Fibroblast growth factor 21 is a metabolic regulator that plays a role in the adaptation to ketosis. Am J Clin Nutr. (2011) 93(Suppl.):901S–5S. doi: 10.3945/ajcn.110.001941 PubMed Abstract | CrossRef Full Text | Google Scholar 8. Klement RJ, Champ CE. Calories, carbohydrates, and cancer therapy with radiation: exploiting the five R's through dietary manipulation. Cancer Metastasis Rev. (2014) 33:217–29. doi: 10.1007/s10555-014-9495-3 PubMed Abstract | CrossRef Full Text | Google Scholar 9. Zilberter T. Carbohydrate-biased control of energy metabolism: the darker side of the selfish brain. Front Neuroenerget. (2011) 3:8. doi: 10.3389/fnene.2011.00008 PubMed Abstract | CrossRef Full Text | Google Scholar 10. Bernhard W, Guzmán-Ruiz M, Layritz C, Legutko B, Zinser E, García-Cáceres C, et al. (2017). Dietary sugar is critical for high fat diet-induction of hypothalamic inflammation via advanced glycation end-products. Mol Metab. 6:897–908. doi: 10.1016/j.molmet.2017.06.008 CrossRef Full Text | Google Scholar 11. Aragno M, Mastrocola R. Dietary sugars and endogenous formation of advanced glycation endproducts: emerging mechanisms of disease. Nutrients (2017) 9:385. doi: 10.3390/nu9040385 PubMed Abstract | CrossRef Full Text | Google Scholar 12. Gao Y, Bielohuby M, Fleming T, Grabner GF, Foppen E, Bernhard W, et al. Dietary sugars, not lipids, drive hypothalamic inflammation. Research Gate. (2017) Available Online at: Researchgate https://goo.gl/kNa4wi (Accessed May 15, 2018). PubMed Abstract | Google Scholar 13. Woodyatt RT. The action of glycol aldehyd and glycerin aldehyd in diabetes mellitus and the nature of antiketogenesis. JAMA (1910) 55:2109–12. doi: 10.1001/jama.1910.04330250005003 CrossRef Full Text | Google Scholar 14. Shaffer PA Antiketogenesis. I. An in vitro analogy. J BiolChem. (1921) 47:433–73. Google Scholar 15. Wilder RM, Winter MD. The threshold of ketogenesis. J Biol Chem. (1922) 52:393–401. Google Scholar 16. Withrow CD. The ketogenic diet: mechanism of anticonvulsant action. Adv Neurol. (1980) 7:635–42. Google Scholar 17. Pandit R, De Jong JW, Vanderschuren LJ, Adan RA. Neurobiology of overeating and obesity: the role of melanocortins and beyond. Eur J Pharmacol. (2011) 660:28–42. doi: 10.1016/j.ejphar.2011.01.034 PubMed Abstract | CrossRef Full Text | Google Scholar 18. Coscina DV, Yehuda S, Dixon LM, Kish SJ, Leprohon-Greenwood CE. Learning is improved by a soybean oil diet in rats. Life Sci. (1986) 38:1789–94. doi: 10.1016/0024-3205(86)90130-X PubMed Abstract | CrossRef Full Text | Google Scholar 19. Finger BC, Dinan TG, Cryan JF. High-fat diet selectively protects against the effects of chronic social stress in the mouse. Neuroscience (2011) 192:351–60. doi: 10.1016/j.neuroscience.2011.06.072 PubMed Abstract | CrossRef Full Text | Google Scholar 20. Funato H, Oda S, Yokofujita J, Igarashi H, Kuroda M. Fasting and high-fat diet alter histone deacetylase expression in the medial hypothalamus. PloS ONE (2011) 6:e18950. doi: 10.1371/journal.pone.0018950 PubMed Abstract | CrossRef Full Text | Google Scholar 21. Holloway CJ, Cochlin LE, Emmanuel Y, Murray A, Codreanu I, Edwards LM, et al. A high-fat diet impairs cardiac high-energy phosphate metabolism and cognitive function in healthy human subjects. Am J Clin Nutr. (2011) 93:748–55. doi: 10.3945/ajcn.110.002758 PubMed Abstract | CrossRef Full Text | Google Scholar 22. Howard JK, Cave BJ, Oksanen LJ, Tzameli I, Bjørbæk C, Flier JS. Enhanced leptin sensitivity and attenuation of diet-induced obesity in mice with haploinsufficiency of Socs3. Nat Med. (2004) 10:734–8. doi: 10.1038/nm1072 PubMed Abstract | CrossRef Full Text | Google Scholar 23. Hu XL, Cheng X, Fei J, Xiong ZQ. Neuron-restrictive silencer factor is not required for the antiepileptic effect of the ketogenic diet. Epilepsia (2011) 52:1609–16. doi: 10.1111/j.1528-1167.2011.03171.x CrossRef Full Text | Google Scholar 24. Jarrett SG, Milder JB, Liang LP, Patel M. The ketogenic diet increases mitochondrial glutathione levels. J Neurochem. (2008) 106:1044–51. doi: 10.1111/j.1471-4159.2008.05460.x PubMed Abstract | CrossRef Full Text | Google Scholar 25. Johnston CS, Tjonn SL, Swan PD, White A, Hutchins H, Sears B. Ketogenic low-carbohydrate diets have no metabolic advantage over nonketogenic low-carbohydrate diets. Am J Clin Nutr. (2006) 83:1055–61. doi: 10.1093/ajcn/83.5.1055 PubMed Abstract | CrossRef Full Text | Google Scholar 26. Johnstone AM, Horgan GW, Murison SD, Bremner DM, Lobley GE. Effects of a high-protein ketogenic diet on hunger, appetite, and weight loss in obese men feeding ad libitum. Am J Clin Nutr. (2008) 87:44–55. doi: 10.1093/ajcn/87.1.44 PubMed Abstract | CrossRef Full Text | Google Scholar 27. Kashiwaya Y, Pawlosky R, Markis W, King MT, Bergman C, Srivastava S, et al. A ketone ester diet increases brain malonyl-CoA and uncoupling proteins 4 and 5 while decreasing food intake in the normal wistar rat. J Biol Chem. (2010) 285:25950–6. doi: 10.1074/jbc.M110.138198 PubMed Abstract | CrossRef Full Text | Google Scholar 28. Kennedy AR, Pissios P, Otu H, Xue B, Asakura K, Furukawa N, et al. A high-fat, ketogenic diet induces a unique metabolic state in mice. Am J Physiol Endocrinol Metab. (2007) 292:E1724–39. doi: 10.1152/ajpendo.00717.2006 PubMed Abstract | CrossRef Full Text | Google Scholar 29. Klöckener T, Hess S, Belgardt BF, Paeger L, Verhagen LA, Husch A, et al. High-fat feeding promotes obesity via insulin receptor/PI3K-dependent inhibition of SF-1 VMH neurons. Nat Neurosci. (2011) 14:911–8. doi: 10.1038/nn.2847 PubMed Abstract | CrossRef Full Text | Google Scholar 30. Könner AC, Janoschek R, Plum L, Jordan SD, Rother E, Ma X, et al. Insulin action in AgRP-expressing neurons is required for suppression of hepatic glucose production. Cell Metab. (2007) 5:438–49. doi: 10.1016/j.cmet.2007.05.004 PubMed Abstract | CrossRef Full Text | Google Scholar 31. Koranda JL, Ruskin DN, Masino SA, Blaise JH. A ketogenic diet reduces long-term potentiation in the dentate gyrus of freely behaving rats. J Neurophysiol. (2011) 106:662–6. doi: 10.1152/jn.00001.2011 PubMed Abstract | CrossRef Full Text | Google Scholar 32. Langdon KD, Clarke J, Corbett D. Long-term exposure to high fat diet is bad for your brain: exacerbation of focal ischemic brain injury. Neuroscience (2011) 182:82–7. doi: 10.1016/j.neuroscience.2011.03.028 PubMed Abstract | CrossRef Full Text | Google Scholar 33. Myers TM, Langston JL. Diet composition exacerbates or attenuates soman toxicity in rats: implied metabolic control of nerve agent toxicity. Neurotoxicology (2011) 32:342–9. doi: 10.1016/j.neuro.2011.03.001 PubMed Abstract | CrossRef Full Text | Google Scholar 34. Nabbout R, Copioli C, Chipaux M, Chemaly N, Desguerre I, Dulac O, et al. Ketogenic diet also benefits Dravet syndrome patients receiving stiripentol: a prospective pilot study. Epilepsia (2011) 52:e54–e69. doi: 10.1111/j.1528-1167.2011.03107.x PubMed Abstract | CrossRef Full Text | Google Scholar 35. Park JH, Ahn J, Kim S, Kwon DY, Ha TY. Murine hepatic miRNAs expression and regulation of gene expression in diet-induced obese mice. Mol Cells (2011) 31:33–8. doi: 10.1007/s10059-011-0009-7 PubMed Abstract | CrossRef Full Text | Google Scholar 36. Park S, Kim DS, Daily JW. Central infusion of ketone bodies modulates body weight and hepatic insulin sensitivity by modifying hypothalamic leptin and insulin signaling pathways in type 2 diabetic rats. Brain Res. (2011) 1401:95–103. doi: 10.1016/j.brainres.2011.05.040 PubMed Abstract | CrossRef Full Text | Google Scholar 37. Posey KA, Clegg DJ, Printz RL, Byun J, Morton GJ, Vivekanandan-Giri A, et al. Hypothalamic proinflammatory lipid accumulation, inflammation, and insulin resistance in rats fed a high-fat diet. Am J Physiol Endocrinol Metab. (2009) 296:E1003–12. doi: 10.1152/ajpendo.90377.2008 PubMed Abstract | CrossRef Full Text | Google Scholar 38. Privitera GJ, Zavala AR, Sanabria F, Sotak KL. High fat diet intake during pre and periadolescence impairs learning of a conditioned place preference in adulthood. Behav Brain Funct. (2011) 7:21. doi: 10.1186/1744-9081-7-21 PubMed Abstract | CrossRef Full Text | Google Scholar 39. Puchowicz MA, Zechel JL, Valerio J, Emancipator DS, Xu K, Pundik S, et al. Neuroprotection in diet-induced ketotic rat brain after focal ischemia. J Cereb Blood Flow Metab. (2008) 28:1907–16. doi: 10.1038/jcbfm.2008.79 PubMed Abstract | CrossRef Full Text | Google Scholar 40. Ravussin Y, Gutman R, Diano S, Shanabrough M, Borok E, Sarman B, et al. Effects of chronic weight perturbation on energy homeostasis and brain structure in mice. Am J Physiol Regul Integr Comp Physiol. (2011) 300:R1352–62. doi: 10.1152/ajpregu.00429.2010 PubMed Abstract | CrossRef Full Text | Google Scholar 41. Samala R, Klein J, Borges K. The ketogenic diet changes metabolite levels in hippocampal extracellular fluid. Neurochem Int. (2011) 58:5–8. doi: 10.1016/j.neuint.2010.10.011 PubMed Abstract | CrossRef Full Text | Google Scholar 42. Samala R, Willis S, Borges K. Anticonvulsant profile of a balanced ketogenic diet in acute mouse seizure models. J Epilepsy Res. (2008) 81:119–27. doi: 10.1016/j.eplepsyres.2008.05.001 PubMed Abstract | CrossRef Full Text | Google Scholar 43. Tanaka T, Hidaka S, Masuzaki H, Yasue S, Minokoshi Y, Ebihara K, et al. Skeletal muscle AMP-activated protein kinase phosphorylation parallels metabolic phenotype in leptin transgenic mice under dietary modification. Diabetes (2005) 54:2365–74. doi: 10.2337/diabetes.54.8.2365 PubMed Abstract | CrossRef Full Text | Google Scholar 44. Teegarden SL, Scott AN, Bale TL. Early life exposure to a high fat diet promotes long-term changes in dietary preferences and central reward signaling. Neuroscience (2009) 162:924–32. doi: 10.1016/j.neuroscience.2009.05.029 PubMed Abstract | CrossRef Full Text | Google Scholar 45. Van der Auwera I, Wera S, Van Leuven F, Henderson ST. A ketogenic diet reduces amyloid beta 40 and 42 in a mouse model of Alzheimer's disease. Nutr Metab. (2005) 2:28. doi: 10.1186/1743-7075-2-28 PubMed Abstract | CrossRef Full Text | Google Scholar 46. Vucetic Z, Kimmel J, Reyes TM. Chronic high-fat diet drives postnatal epigenetic regulation of μ-opioid receptor in the brain. Neuropsychopharmacology (2011) 36:1199–206. doi: 10.1038/npp.2011.4 PubMed Abstract | CrossRef Full Text | Google Scholar 47. Wang C, Bomberg E, Billington CJ, Levine AS, Kotz CM. Brain-derived neurotrophic factor (BDNF) in the hypothalamic ventromedial nucleus increases energy expenditure. Brain Res. (2010) 1336:66–77. doi: 10.1016/j.brainres.2010.04.013 PubMed Abstract | CrossRef Full Text | Google Scholar 48. Winocur G, Greenwood CE. Studies of the effects of high fat diets on cognitive function in a rat model. Neurobiol Aging (2005) 26 (Suppl. 1):46–9. doi: 10.1016/j.neurobiolaging.2005.09.003 PubMed Abstract | CrossRef Full Text | Google Scholar 49. Wood JD, Nute GR, Richardson RI, Whittington FM, Southwood O, Plastow G, et al. Effects of breed, diet and muscle on fat deposition and eating quality in pigs. Meat Sci. (2004) 67:651–67. doi: 10.1016/j.meatsci.2004.01.007 PubMed Abstract | CrossRef Full Text | Google Scholar 50. Yamada KA, Rensing N, Thio LL. Ketogenic diet reduces hypoglycemia-induced neuronal death in young rats. Neurosci Lett. (2005) 385:210–4. doi: 10.1016/j.neulet.2005.05.038 PubMed Abstract | CrossRef Full Text | Google Scholar 51. Ebbeling CB, Swain JF, Feldman HA, Wong WW, Hachey DL, Garcia-Lago E, et al. Effects of dietary composition on energy expenditure during weight-loss maintenance. JAMA (2012) 307:2627–34. doi: 10.1001/jama.2012.6607 PubMed Abstract | CrossRef Full Text | Google Scholar 52. Gardner CD, Kiazand A, Alhassan S, Kim S, Stafford RS, Balise RR, et al. Comparison of the Atkins, Zone, Ornish, and LEARN diets for change in weight and related risk factors among overweight premenopausal women: the A To Z Weight Loss Study: a randomized trial. JAMA (2007) 297:969–77. doi: 10.1001/jama.297.9.969 PubMed Abstract | CrossRef Full Text | Google Scholar 53. Gardner CD, Kim S, Bersamin A, Dopler-Nelson M, Otten J, Oelrich B, et al. Micronutrient quality of weight-loss diets that focus on macronutrients: results from the A TO Z study. Am J Clin Nutr. (2010) 92:304–12. doi: 10.3945/ajcn.2010.29468 PubMed Abstract | CrossRef Full Text | Google Scholar 54. Dansinger ML, Gleason JA, Griffith JL, Selker HP, Schaefer EJ. Comparison of the Atkins, Ornish, weight watchers, and Zone diets for weight loss and heart disease risk reduction: a randomized trial. JAMA (2005) 293:43–53. doi: 10.1001/jama.293.1.43 PubMed Abstract | CrossRef Full Text | Google Scholar 55. Kossoff EH, Dorward JL. The modified atkins diet. Epilepsia (2008) 49(Suppl. 8):37–41. doi: 10.1111/j.1528-1167.2008.01831.x CrossRef Full Text | Google Scholar 56. Kossoff EH, Bosarge JL, Miranda MJ, Wiemer-Kruel A, Kang HC, Kim HD. Will seizure control improve by switching from the modified Atkins diet to the traditional ketogenic diet. Epilepsia (2010) 51:2496–9. doi: 10.1111/j.1528-1167.2010.02774.x PubMed Abstract | CrossRef Full Text | Google Scholar 57. Kossoff EH, Cervenka MC, Henry BJ, Haney CA, Turner Z. A decade of the modified Atkins diet (2003–2013): results, insights, and future directions. Epilepsy Behav. (2013) 29:437–42. doi: 10.1016/j.yebeh.2013.09.032 PubMed Abstract | CrossRef Full Text | Google Scholar 58. Ito S, Oguni H, Ito Y, Ishigaki K, Ohinata J, Osawa M. Modified Atkins diet therapy for a case with glucose transporter 1 deficiency syndrome. Brain Dev. (2008) 30:226–8. doi: 10.1016/j.braindev.2007.08.006 PubMed Abstract | CrossRef Full Text | Google Scholar 59. El-Rashidy OF, Nassar MF, Abdel-Hamid IA, Shatla RH, Abdel-Hamid MH, Gabr SS, et al. Modified Atkins diet vs. classic ketogenic formula in intractable epilepsy. Acta Neurol Scand. (2013) 128, 402–8. doi: 10.1111/ane.12137 PubMed Abstract | CrossRef Full Text | Google Scholar 60. Ye F, Li XJ, Jiang WL, Sun HB, Liu J. Efficacy of and patient compliance with a ketogenic diet in adults with intractable epilepsy: a meta-analysis. J Neurol. (2015) 11:26–31. doi: 10.3988/jcn.2015.11.1.26 PubMed Abstract | CrossRef Full Text | Google Scholar 61. Kirsch JR, D'Alecy LG. Hypoxia induced preferential ketone utilization by rat brain slices. Stroke (1984) 15:319–23. doi: 10.1161/01.STR.15.2.319 PubMed Abstract | CrossRef Full Text | Google Scholar 62. Veech RL, Chance B, Kashiwaya Y, Lardy HA, Cahill GF Jr. (2001) Ketone bodies, potential therapeutic uses. IUBMB Life 51:241–7. doi: 10.1080/152165401753311780 PubMed Abstract | CrossRef Full Text | Google Scholar 63. Bough KJ, Wetherington J, Hassel B, Pare JF, Gawryluk JW, et al. Mitochondrial biogenesis in the anticonvulsant mechanism of the ketogenic diet. Ann Neurol. (2006) 60:223–35. doi: 10.1002/ana.20899 PubMed Abstract | CrossRef Full Text | Google Scholar 64. Puchowicz MA, Xu K, Sun X, Ivy A, Emancipator D, Lamanna JC. Diet-induced ketosis increases capillary density without altered blood flow in rat brain. Am J Physiol Endocrinol Metab. (2007) 292:E1607–15. doi: 10.1152/ajpendo.00512.2006 PubMed Abstract | CrossRef Full Text | Google Scholar 65. Haces ML, Hernandez-Fonseca K, Medina-Campos ON, Montiel T, Pedraza-Chaverri J, Massieu L. Antioxidant capacity contributes to protection of ketone bodies against oxidative damage induced during hypoglycemic conditions. Exp Neurol. (2008) 211:85–96. doi: 10.1016/j.expneurol.2007.12.029 PubMed Abstract | CrossRef Full Text | Google Scholar 66. Schutz PW, Wong PK, O'Kusky J, Innis SM, Stockler S. Effects of d-3-hydroxybutyrate treatment on hypoglycemic coma in rat pups. Exp Neurol. (2011) 227:180–7. doi: 10.1016/j.expneurol.2010.10.013 PubMed Abstract | CrossRef Full Text | Google Scholar 67. Kim do Y, Vallejo J, Rho JM. Ketones prevent synaptic dysfunction induced by mitochondrial respiratory complex inhibitors. J Neurochem. (2010) 114:130–41. doi: 10.1111/j.1471-4159.2010.06728.x PubMed Abstract | CrossRef Full Text | Google Scholar 68. Barks JD, Liu Y, Shangguan Y, Djuric Z, Ren J, Silverstein FS. Maternal high-fat diet influences outcomes after neonatal hypoxic-ischemic brain injury in rodents. J Cereb Blood Flow Metab. (2016) 37:307–18. doi: 10.1177/0271678X15624934 PubMed Abstract | CrossRef Full Text | Google Scholar 69. Goldbart AD, Row BW, Kheirandish-Gozal L, Cheng Y, Brittian KR, Gozal D. High fat/refined carbohydrate diet enhances the susceptibility to spatial learning deficits in rats exposed to intermittent hypoxia. Brain Res. (2006) 1090:190–6. doi: 10.1016/j.brainres.2006.03.046 PubMed Abstract | CrossRef Full Text | Google Scholar 70. Vanitallie TB, Nonas C, Di Rocco A, Boyar K, Hyams K, Heymsfield SB. Treatment of Parkinson disease with diet-induced hyperketonemia: a feasibility study. Neurology (2005) 64:728–30. doi: 10.1212/01.WNL.0000152046.11390.45 PubMed Abstract | CrossRef Full Text | Google Scholar 71. Murphy P, Likhodii S, Nylen K, Burnham WM. The antidepressant properties of the ketogenic diet. Biol Psychiatry (2004) 56:981–3. doi: 10.1016/j.biopsych.2004.09.019 PubMed Abstract | CrossRef Full Text | Google Scholar 72. Murphy P, Likhodii SS, Hatamian M, McIntyre Burnham W. Effect of the ketogenic diet on the activity level of Wistar rats. Pediatr Res. (2005) 57:353–7. doi: 10.1203/01.PDR.0000150804.18038.79 PubMed Abstract | CrossRef Full Text | Google Scholar 73. Henderson ST, Vogel JL, Barr LJ, Garvin F, Jones JJ, Costantini LC. Study of the ketogenic agent AC-1202 in mild to moderate Alzheimer's disease: a randomized, double-blind, placebo-controlled, multicenter trial. Nutr Metab. (2009) 6:31. doi: 10.1186/1743-7075-6-31 PubMed Abstract | CrossRef Full Text | Google Scholar 74. de Lau LM, Bornebroek M, Witteman JC, Hofman A, Koudstaal PJ, Breteler MM. Dietary fatty acids and the risk of Parkinson disease: the Rotterdam study. Neurology (2005) 64:2040–5. doi: 10.1212/01.WNL.0000166038.67153.9F PubMed Abstract | CrossRef Full Text | Google Scholar 75. Maalouf M, Rho JM. Oxidative impairment of hippocampal long-term potentiation involves activation of protein phosphatase 2A and is prevented by ketone bodies. J Neurosci Res. (2008) 86:3322–30. doi: 10.1002/jnr.21782 PubMed Abstract | CrossRef Full Text | Google Scholar 76. Page KA, Williamson A, Yu N, McNay EC, Dzuira J, McCrimmon RJ, et al. Medium chain fatty acids improve cognitive function in intensively treated type 1 diabetic patients and support in vitro synaptic transmission during acute hypoglycemia. Diabetes (2009) 58:1237–44. doi: 10.2337/db08-1557 PubMed Abstract | CrossRef Full Text | Google Scholar 77. White H, Venkatesh B. Clinical review: ketones and brain injury. Crit Care (2011) 15:219. doi: 10.1186/cc10020 PubMed Abstract | CrossRef Full Text | Google Scholar 78. Maalouf M, Rho JM, Mattson MP. The neuroprotective properties of calorie restriction, the ketogenic diet, and ketone bodies. Brain Res Rev. (2009) 59:293–315. doi: 10.1016/j.brainresrev.2008.09.002 PubMed Abstract | CrossRef Full Text | Google Scholar 79. Krikorian R, Shidler MD, Dangelo K, Couch SC, Benoit SC, Cleggc DJ. Dietary ketosis enhances memory in mild cognitive impairment. Neurobiol Aging (2012) 33:425.e19–e27. doi: 10.1016/j.neurobiolaging.2010.10.006 PubMed Abstract | CrossRef Full Text | Google Scholar 80. Iacovides S, Meiring RM. The effect of a ketogenic diet versus a high-carbohydrate, low-fat diet on sleep, cognition, thyroid function, and cardiovascular health independent of weight loss: study protocol for a randomized controlled trial. Trials (2018) 19:62. doi: 10.1186/s13063-018-2462-5 PubMed Abstract | CrossRef Full Text | Google Scholar 81. McPherson PA, McEneny J. The biochemistry of ketogenesis and its role in weight management, neurological disease and oxidative stress. J Physiol Biochem. (2011) 68:141–51. doi: 10.1007/s13105-011-0112-4 PubMed Abstract | CrossRef Full Text | Google Scholar 82. Miller VJ, Villamena FA, Volek JS. Nutritional ketosis and mitohormesis: potential implications for mitochondrial function and human health. J Clin Nutr Metab. (2018) 2018:5157645. doi: 10.1155/2018/5157645 PubMed Abstract | CrossRef Full Text | Google Scholar 83. Huttenlocher PR. Ketonemia and seizures: metabolic and anticonvulsant effects of two ketogenic diets in childhood epilepsy. Pediatr Res. (1976) 10:536–40. doi: 10.1203/00006450-197605000-00006 PubMed Abstract | CrossRef Full Text | Google Scholar 84. Newman JC, Kroll F, Ulrich S, Palop JJ, Verdin E. Ketogenic diet or BHB improves epileptiform spikes, memory, survival in Alzheimer's model. bioRxiv (2017) 136226:1–30. doi: 10.1101/136226 CrossRef Full Text | Google Scholar 85. Bostock E, Kirkby KC, Taylor BV. The current status of the ketogenic diet in psychiatry. Front Psychiatry (2017) 8:43. doi: 10.3389/fpsyt.2017.00043 PubMed Abstract | CrossRef Full Text | Google Scholar 86. Bough KJ, Rho JM. Anticonvulsant mechanisms of the ketogenic diet. Epilepsia (2007) 48:43–58. doi: 10.1111/j.1528-1167.2007.00915.x PubMed Abstract | CrossRef Full Text | Google Scholar 87. Samokhina E, Popova I, Malkov A, Ivanov AI, Papadia D, Osypov A, et al. Chronic inhibition of brain glycolysis initiates epileptogenesis. J Neurosci Res. (2017) 95:2195–206. doi: 10.1002/jnr.24019 PubMed Abstract | CrossRef Full Text | Google Scholar 88. Zilberter Y, Zilberter M. The vicious circle of hypometabolism in neurodegenerative diseases: ways and mechanisms of metabolic correction. J Neurosci Res. (2017) 95:2217–35. doi: 10.1002/jnr.24064 PubMed Abstract | CrossRef Full Text | Google Scholar 89. Bozzetti F, Zupec-Kania B. Toward a cancer-specific diet. Clin Nutr. (2015) 35:1188–95 doi: 10.1016/j.clnu.2015.01.013 PubMed Abstract | CrossRef Full Text | Google Scholar 90. Haller S, Jasper H. You are what you eat: linking high-fat diet to stem cell dysfunction and tumorigenesis. Cell Stem Cell (2016) 18:564–6. doi: 10.1016/j.stem.2016.04.010 PubMed Abstract | CrossRef Full Text | Google Scholar 91. Kwon OJ, Zhang B, Zhang L, Xin L. High fat diet promotes prostatic basal-to-luminal differentiation and accelerates initiation of prostate epithelial hyperplasia originated from basal cells. Stem Cell Res. (2016) 16:682–91. doi: 10.1016/j.scr.2016.04.009 PubMed Abstract | CrossRef Full Text | Google Scholar 92. Chen GL, Luo Y, Eriksson D, Meng X, Qian C, Bäuerle T, et al. High fat diet increases melanoma cell growth in the bone marrow by inducing osteopontin and interleukin 6. Differentiation (2016) 24:1–17 doi: 10.18632/oncotarget.8474 CrossRef Full Text | Google Scholar 93. Labbe DP, Zadra G, Yang M, Lin CY, Reyes JM, Cacciatore S, et al. High fat diet accelerates MYC-driven prostate cancer through metabolic and epigenomic rewiring. Cancer Res. (2016) 76(Suppl. 14):2674. doi: 10.1158/1538-7445.AM2016-2674 CrossRef Full Text | Google Scholar 94. Brehm BJ, Seeley RJ, Daniels SR, D'alessio DA. A randomized trial comparing a very low carbohydrate diet and a calorie-restricted low fat diet on body weight and cardiovascular risk factors in healthy women. J Clin Endocrinol Metab. (2003) 88, 1617–23. doi: 10.1210/jc.2002-021480 PubMed Abstract | CrossRef Full Text | Google Scholar 95. Jeon BT, Jeong EA, Shin HJ, Lee Y, Lee DH, Kim HJ, et al. (2012). Resveratrol attenuates obesity-associated peripheral and central inflammation and improves memory deficit in mice fed a high-fat diet. Diabetes 61:1444–54. doi: 10.2337/db11-1498 PubMed Abstract | CrossRef Full Text | Google Scholar 96. Yancy WS, Foy M, Chalecki AM, Vernon MC, Westman EC. A low-carbohydrate, ketogenic diet to treat type 2 diabetes. Nutr Metab. (2005) 2:34. doi: 10.1186/1743-7075-2-34 PubMed Abstract | CrossRef Full Text | Google Scholar 97. Nielsen JV, Joensson EA. Low-carbohydrate diet in type 2 diabetes: stable improvement of bodyweight and glycemic control during 44 months follow-up. Nutr Metab. (2008) 5:14. doi: 10.1186/1743-7075-5-14 PubMed Abstract | CrossRef Full Text | Google Scholar 98. Basu S, Yoffe P, Hills N, Lustig RH. The relationship of sugar to population-level diabetes prevalence: an econometric analysis of repeated cross-sectional data. PloS ONE (2013) 8:e57873. doi: 10.1371/journal.pone.0057873 PubMed Abstract | CrossRef Full Text | Google Scholar 99. Abudushalamu Y, Visnagri A, Viswambharan H, Bonthron D, Kearney M, Asipu A. P24 sucrose-and high fat-induced insulin resistance leads to endothelial dysfunction and is associated with ketohexokinase activation. Heart (2016) 102:A9. doi: 10.1136/heartjnl-2016-310696.28 CrossRef Full Text | Google Scholar 100. Talbot FB, Metkalf KM, Moriarty ME. Epilepsy: chemical investigations of rational treatment by production of ketosis. Am J Dis Child. (1927) 33:218–25. doi: 10.1001/archpedi.1927.04130140038005 CrossRef Full Text | Google Scholar 101. Berthoud HR, Lenard NR, Shin AC. Food reward, hyperphagia, and obesity. Am J Physiol Regul Integr Comp Physiol. (2011) 300:R1266–77 doi: 10.1152/ajpregu.00028.2011 PubMed Abstract | CrossRef Full Text | Google Scholar 102. Westman EC, Mavropoulos J, Yancy WS, Volek JS. A review of low-carbohydrate ketogenic diets. Curr Atheroscler Rep. (2003) 5:476–83. doi: 10.1007/s11883-003-0038-6 PubMed Abstract | CrossRef Full Text | Google Scholar 103. Martin B, Ji S, Maudsley S, Mattson MP. “Control” laboratory rodents are metabolically morbid: why it matters. Proc Natl Acad Sci USA. (2010) 107:6127–33. doi: 10.1073/pnas.0912955107 PubMed Abstract | CrossRef Full Text | Google Scholar 104. Noakes TD, Windt J. Evidence that supports the prescription of low-carbohydrate high-fat diets: a narrative review. Br J Sports Med. (2017) 51:133–9. doi: 10.1136/bjsports-2016-096491 PubMed Abstract | CrossRef Full Text | Google Scholar 105. Gardener SL, Rainey-Smith SR, Sohrabi HR, Weinborn M, Verdile G, Fernando WMAD, et al. Increased carbohydrate intake is associated with poorer performance in verbal memory and attention in an APOE genotype-dependent manner. J Alzheimers Dis. (2017) 58:193–201. doi: 10.3233/JAD-161158 PubMed Abstract | CrossRef Full Text | Google Scholar 106. Francis HM, Stevenson RJ. Potential for diet to prevent and remediate cognitive deficits in neurological disorders. Nutr Rev. (2018) 76:204–17. doi: 10.1093/nutrit/nux073 PubMed Abstract | CrossRef Full Text | Google Scholar 107. Bueno NB, de Melo ISV, de Oliveira SL, da Rocha Ataide T. Very-low-carbohydrate ketogenic diet v. low-fat diet for long-term weight loss: a meta-analysis of randomised controlled trials. Br J Nutr. (2013) 110:1178–87. doi: 10.1017/S0007114513000548 PubMed Abstract | CrossRef Full Text | Google Scholar 108. Wojnicki FH, Charny G, Corwin RL. Binge-type behavior in rats consuming trans-fat-free shortening. Physiol Behav. (2008) 94:627–9. doi: 10.1016/j.physbeh.2008.03.016 PubMed Abstract | CrossRef Full Text | Google Scholar 109. Pickering C, Alsio J, Hulting AL, Schioth HB. Withdrawal from free-choice high-fat high-sugar diet induces craving only in obesity-prone animals. Psychopharmacology (2009) 204:431–43. doi: 10.1007/s00213-009-1474-y PubMed Abstract | CrossRef Full Text | Google Scholar 110. Zilberter T. Food addiction and obesity: do macronutrients matter? Front Neuroenerg. (2012) 4:7. doi: 10.3389/fnene.2012.00007 PubMed Abstract | CrossRef Full Text | Google Scholar Keywords: diet classification, ketogenic threshold, anti-ketogenic threshold, macronutrients, metabolic effects of diets Citation: Zilberter T and Zilberter Y (2018) Ketogenic Ratio Determines Metabolic Effects of Macronutrients and Prevents Interpretive Bias. Front. Nutr. 5:75. doi: 10.3389/fnut.2018.00075 Received: 29 May 2018; Accepted: 07 August 2018; Published: 30 August 2018. Edited by: Reviewed by: Copyright © 2018 Zilberter and Zilberter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Yuri Zilberter, [email protected]
, Ronald W. Davis
Published: 2 August 2018
Frontiers in Physiology, Volume 9; https://doi.org/10.3389/fphys.2018.01040

Abstract:
Parkinson's disease (PD) is a neurodegenerative disease caused by a reduction in dopaminergic neurons in the substantia nigra (Dexter and Jenner, 2013). Individuals can exhibit muscle tremors, rigidity, and bradykinesia, leading to posture and gait abnormalities (Dexter and Jenner, 2013). Although the incidence ratio of males to females is ~1.46 (Taylor et al., 2007), the prevalence of PD among men is doubled when compared to women (Elbaz et al., 2002). A lower mortality rate has traditionally been associated with PD in women due to the overall longer life expectancy when compared to men in the general population (Xu et al., 2014; Pinter et al., 2015). However, a diagnosis of PD was found to be associated with a two-fold increased risk for all-cause mortality in a recent study with 396 older women (Winter et al., 2016). This value is similar to that reported among older men (Xu et al., 2014). Males may have a greater predisposition to develop PD (Gillies et al., 2014). This bias may be due to the molecular pathology of PD. Gene expression profiles in dopaminergic neurons are sex-specific, and the survivability of these neurons are dependent on molecular pathways that are very different in men and women (Gillies et al., 2014). In healthy brain tissue, genes involved in signal transduction and neuronal growth are up-regulated more in women (Cantuti-Castelvetri et al., 2007). In men, genes with specific mutations (e.g., α-synuclein, PINK-1) that may contribute to the pathogenesis of PD are upregulated more often (Simunovic et al., 2010). A downregulation of genes that provide for oxidative phosphorylation, and synaptic and nerve impulse transmission, is also more prevalent in older males (Simunovic et al., 2010). Therefore, women are thought to have greater protection from PD when compared to men, which may also be due to estrogen concentrations (Gillies et al., 2014). Estrogen influences dopamine synthesis and release while inhibiting dopamine uptake (Shulman, 2007). The higher concentrations of estrogen are a possible reason for the more benign phenotype in women when compared to men, particularly before a course of medication has begun (Haaxma et al., 2007; Miller and Cronin-Golomb, 2011; Cereda et al., 2013). Estrogen may play a role in preventing toxins from possibly degrading neurons in the substantia nigra (Shulman, 2007). Exogenous or endogenous estrogen administration may therefore play a crucial role in the pathogenesis of PD (Lv et al., 2017). There are several factors, including medicinal options, surgical interventions, dietary strategies, and lifestyle habits that may alter estrogen levels in women with PD. Oral contraceptive (OC) use may increase the risk of PD among women (Nicoletti et al., 2011), with a 20% increased risk of developing PD for every five years of OC use (Simon et al., 2009). However, the use of OCs has been also been shown to be inversely associated with PD risk (Greene et al., 2014), with continuous use for more than 10 years determined to even be a protective mechanism against developing PD (Liu et al., 2014). Following an ovariectomy, a decrease in the concentration of estrogen receptor-α and an increase in angiotensin, NADPH-oxidase activity, and the expression of neuroinflammatory markers was observed in the substantia nigra of menopausal rats (Rodriguez-Perez et al., 2015). Smoking may increase levels of estrogen and serum sex hormone-binding globulin, and may also be protective against PD, as an inverse correlation between smoking and PD risk has been shown (Ritz et al., 2007; Breckenridge et al., 2016). Proper intake of vitamin D may increase glial derived neurotrophic factor (Smith et al., 2006) and reduce activated microglial cells (Kim et al., 2006), therefore acting as a neuroprotectant with estrogen and reducing inflammation. The protective effect of estrogen is not always evident, particularly in women who report a more rapid onset of motor symptoms upon diagnosis (Sato et al., 2006; Colombo et al., 2015; Bjornestad et al., 2016). With regard to motor symptoms, women typically present more with tremors and bradykinesia, but not rigidity, when compared to men (Haaxma et al., 2007; Martinez-Martin et al., 2012). Non-motor symptoms, such as constipation, restless legs, pain, nervousness, anxiety, and sadness, are more prevalent in women (Martinez-Martin et al., 2012; Solla et al., 2012; Picillo et al., 2013; Szewczyk-Krolikowski et al., 2014). A reduction in visuospatial cognition also occurs more frequently in women (Miller and Cronin-Golomb, 2011). To treat the motor and non-motor symptoms of PD, individuals are often presented with a variety of options. Medications and surgical procedures are available but are often unsuccessful at treating all symptoms of PD, can be expensive and invasive, and may lead to unwanted side effects (Bloem et al., 2004). Exercise may therefore be an inexpensive and complementary option to other interventions to treat symptoms of PD. Performing regular exercise may improve numerous functional outcome measures and quality-of-life, particularly in higher-functioning individuals diagnosed with neurodegenerative diseases, including PD, multiple sclerosis, and dementia (Dodd et al., 2011; Brienesse and Emerson, 2013; Morley et al., 2015). Gait speed is slower in older women, possibly due to observed increases in muscle strength and standing balance ability in men (Bohannon, 1997). When compared to men with PD, women with PD exhibit increased cadence and decreased stride length and frequency (Kokko et al., 1997; Pedersen et al., 1997). To improve gait, motor performance, and quality-of-life, aerobic exercise on a motorized treadmill may be effective intervention for those with PD (Herman et al., 2007). Treadmill exercise can improve motor function, stride and swing time variability, and gait speed in adults with mild to moderate PD (Herman et al., 2007). This in turn may allow for greater mobility during activities of daily living (ADLs) and a reduced incidence of falls (Cakit et al., 2007; Herman et al., 2007). Performance during ADLs may be enhanced more with the use of external sensory cues during exercise (Cassimatis et al., 2016). These auditory, visual, or tactile cues bypass the basal ganglia using alternative pathways (e.g., cortical, parietopremotor) when processed, thus creating an additive improvement in ADL performance (Kones, 2010). Resistance and flexibility exercises are also recommended for individuals diagnosed with PD. Resistance exercise training can improve muscle strength, muscular endurance, fat-free mass, balance and mobility, and walking economy in adults with PD (Brienesse and Emerson, 2013). When assessing lower limb strength, Pääsuke et al. (2002) found that reaction times and maximum isometric force values were different between legs in women with PD. This may be due to the postural and balance impairments that are prevalent with a diagnosis of PD. Using regression analysis, Schenkman et al. (1998) found a greater correlation between women with PD and poor spinal flexibility and balance when compared to men with PD. Alternative modalities of exercise, including Nordic walking (Cugusi et al., 2017), tai chi (Hackney and Earhart, 2008), and tango dancing (Hackney et al., 2007), may also elicit improvements in balance and mobility in those with PD. Virtual reality (VR) is a newer technology that may be integrated into a rehabilitative program for those with PD. By allowing for repetitive movement that targets both motor and cognitive performance, VR may allow for the learning or re-learning of motor strategies that have been lost due to the progression of PD (Goble et al., 2014). Traditional aerobic and resistance exercise should be prescribed differently to women diagnosed with PD due to the possible inherent sex differences in this population. Current exercise guidelines for adults with PD and proposed guidelines for women with PD are found in Table 1. As PD is a risk factor for osteoporosis, appropriate aerobic modalities should be chosen for women with PD. Short, supervised walks on a level surface with occasional turns (e.g., an athletic track), to add variation to the direction of loads, may be the best aerobic exercise modality that can be prescribed to women with PD. If not ambulatory, leg ergometry may be a suitable option, with a focus on resistance rather than speed. Intensity should be prescribed using VO2 reserve (if available) or rate of perceived exertion (RPE). Prescribing intensity using heart rate responses should not be recommended, as many individuals with PD exhibit autonomic nervous system (ANS) dysfunction (e.g., sympathetic denervation) and are prescribed medications that can interfere with heart rate responses and heart rate variability (Ziemssen and Reichmann, 2010; Moore et al., 2016). This ANS dysfunction is thought to contribute to fatigue in those with PD (Nakamura et al., 2011). Session duration and frequency, particularly at the start of an exercise program, should therefore be limited. Table 1. Exercise guidelines for adults diagnosed with Parkinson's disease. Squats using only body weight or with additional weight may be the most appropriate resistance exercise modality for women with PD. Squats are closed-chain kinetic exercises, which may be a more suitable exercise due to the abnormal coordination between limbs caused by a greater prevalence of tremors at rest in women with PD (Ehrman et al., 2013). The strength of the quadriceps, the prime movers during the upward phase of a squat, is correlated with functional abilities and performance during ADLs (Moore et al., 2016). Targeting the quadriceps musculature during exercise is beneficial because the extensors typically exhibit more muscle weakness than the flexors, and there is evidence that proximal muscles are weaker than distal muscles in those with PD (Inkster et al., 2003; Cano-de-la-Cuerda et al., 2010). The induced compression during the exercise may also improve bone mineral density (BMD) at the hip. Machine weights may be an added supplement to resistance training once increases in volume and frequency can be tolerated. Flexibility training needs to be incorporated into an exercise program for those with PD. A combination of stretching and resistance exercise can improve muscle strength and gait speed in this population (Shulman et al., 2013). As women with PD typically exhibit more frequent episodes of bradykinesia, increasing the strength of skeletal muscles (particularly lower body) and gait parameters is crucial (Haaxma et al., 2007). The Office of Research on Women's Health (ORWH) within the National Institutes of Health (NIH) has called for research scientists to consider sex when designing study protocols to allow for both men and women to receive the full benefit of medical research (National Institutes of Health, 2017). Exercise is an effective intervention that can elicit functional changes in adults with PD. Women with PD should follow an exercise prescription that somewhat deviates from the current exercise guidelines, which are not categorized according to sex. This deviation is due in part to the differences observed with the timing of PD onset and progression, primary symptoms, and hormonal changes that are exhibited in women with PD. A comparison of the physiological effects of exercise training between men and women with PD needs to be further investigated to allow clinicians and health practitioners to correctly prescribe exercise as a long-term option to treat the symptoms of PD. All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. This article was published with support from the Texas Woman's University Libraries' Open Access Fund. Bjornestad, A., Forsaa, E. B., Pedersen, K. F., Tysnes, O. B., Larsen, J. P., and Alves, G. (2016). Risk and course of motor complications in a population-based incident Parkinson's disease cohort. Parkinsonism Relat. Disord. 22, 48–53. doi: 10.1016/j.parkreldis.2015.11.007 PubMed Abstract | CrossRef Full Text | Google Scholar Bloem, B. R., Hausdorff, J. M., Visser, J. E., and Giladi, N. (2004). Falls and freezing of gait in Parkinson's disease: a review of two interconnected, episodic phenomena. Mov. Disord. 19, 871–884. doi: 10.1002/mds.20115 PubMed Abstract | CrossRef Full Text | Google Scholar Bohannon, R. W. (1997). Reference values for extremity muscle strength obtained by hand-held dynamometry from adults aged 20 to 79 years. Arch. Phys. Med. Rehabil. 78, 26–32. doi: 10.1016/S0003-9993(97)90005-8 PubMed Abstract | CrossRef Full Text | Google Scholar Breckenridge, C. B., Berry, C., Chang, E. T., Sielken, R. L., and Mandel, J. S. (2016). Associated between Parkinson's disease and cigarette smoking, rural living, well-water consumption, farming and pesticide use: systematic review and analysis. PLoS ONE 11:e0151841. doi: 10.1371/journal.pone.0151841 PubMed Abstract | CrossRef Full Text | Google Scholar Brienesse, L. A., and Emerson, M. N. (2013). Effects of resistance training for people with Parkinson's disease: a systematic review. J. Am. Med. Dir. Assoc. 14, 236–241. doi: 10.1016/j.jamda.2012.11.012 PubMed Abstract | CrossRef Full Text | Google Scholar Cakit, B. D., Saracoglu, M., Genc, H., Erdem, H. R., and Inan, L. (2007). The effects of incremental speed-dependent treadmill training on postural instability and fear of falling in Parkinson's disease. Clin. Rehabil. 21, 698–705. doi: 10.1177/0269215507077269 PubMed Abstract | CrossRef Full Text | Google Scholar Cano-de-la-Cuerda, R., Pérez-de-Heredia, M., Miangolarra-Page, J. C., Muñoz-Hellín, E., and Fernández-de-Las-Peñas, C. (2010). Is there muscular weakness in Parkinson's disease? Am. J. Phys. Med. Rehabil. 89, 70–76. doi: 10.1097/PHM.0b013e3181a9ed9b PubMed Abstract | CrossRef Full Text | Google Scholar Cantuti-Castelvetri, I., Keller-McGandy, C., Bouzou, B., Asteris, G., Clark, T. W., Frosch, M. P., et al. (2007). Effects of gender on nigral gene expression and parkinson disease. Neurobiol. Dis. 26, 606–614. doi: 10.1016/j.nbd.2007.02.009 PubMed Abstract | CrossRef Full Text | Google Scholar Cassimatis, C., Liu, K. P., Fahey, P., and Bissett, M. (2016). The effectiveness of external sensory cues in improving functional performance in individuals with Parkinson's disease: a systematic review with meta-analysis. Int. J. Rehabil. Res. 39, 211–218. doi: 10.1097/MRR.0000000000000171 PubMed Abstract | CrossRef Full Text | Google Scholar Cereda, E., Barichella, M., Cassani, E., Caccialanza, R., and Pezzoli, G. (2013). Reproductive factors and clinical features of Parkinson's disease. Parkinsonism Relat. Disord. 19, 1094–1099. doi: 10.1016/j.parkreldis.2013.07.020 PubMed Abstract | CrossRef Full Text | Google Scholar Colombo, D., Abbruzzese, G., Antonini, A., Barone, P., Bellia, G., Franconi, F., et al. (2015). The “Gender Factor” in wearing-off among patients with Parkinson's disease: a post hoc analysis of DEEP study. Sci. World J. 2015:787451. doi: 10.1155/2015/787451 PubMed Abstract | CrossRef Full Text | Google Scholar Cugusi, L., Manca, A., Dragone, D., Deriu, F., Solla, P., Secci, C., et al. (2017). Nordic walking for the management of people with Parkinson disease: a systematic review. PM R 9, 1157–1166. doi: 10.1016/j.pmrj.2017.06.021 PubMed Abstract | CrossRef Full Text | Google Scholar Dexter, D. T., and Jenner, P. (2013). Parkinson disease: from pathology to molecular disease mechanisms. Free Radic. Biol. Med. 62, 132–144. doi: 10.1016/j.freeradbiomed.2013.01.018 PubMed Abstract | CrossRef Full Text | Google Scholar Dodd, K. J., Taylor, N. F., Shields, N., Prasad, D., McDonald, E., and Gillon, A. (2011). Progressive resistance training did not improve walking but can improve muscle performance, quality of life and fatigue in adults with multiple sclerosis: a randomized controlled trial. Mult. Scler. 17, 1362–1374. doi: 10.1177/1352458511409084 PubMed Abstract | CrossRef Full Text | Google Scholar Ehrman, J. K., Gordon, P. M., Visich, P. S., and Keteyian, S. J. (2013). Clinical Exercise Physiology. Champaign, IL: Human Kinetics. Google Scholar Elbaz, A., Bower, J. H., Maraganore, D. M., McDonnell, S. K., Peterson, B. J., Ahlskog, J. E., et al. (2002). Risk tables for parkinsonism and Parkinson's disease. J. Clin. Epidemiol. 55, 25–31. doi: 10.1016/S0895-4356(01)00425-5 PubMed Abstract | CrossRef Full Text | Google Scholar Gillies, G. E., Pienaar, I. S., Vohra, S., and Qamhawi, Z. (2014). Sex differences in Parkinson's disease. Front. Neuroendocrinol. 35, 370–384. doi: 10.1016/j.yfrne.2014.02.002 PubMed Abstract | CrossRef Full Text | Google Scholar Goble, D. J., Cone, B. L., and Fling, B. W. (2014). Using the Wii Fit as a tool for balance assessment and neurorehabilitation: the first half decade of “Wii-search.” J. Neuroeng. Rehabil. 11:12. doi: 10.1186/1743-0003-11-12 PubMed Abstract | CrossRef Full Text | Google Scholar Greene, N., Lassen, C. F., Rugbjerg, K., and Ritz, B. (2014). Reproductive factors and Parkinson's disease risk in Danish women. Eur. J. Neurol. 21, 1168–1177. doi: 10.1111/ene.12450 PubMed Abstract | CrossRef Full Text | Google Scholar Haaxma, C. A., Bloem, B. R., Borm, G. F., Oyen, W. J., Leenders, K. L., Eshuis, S., et al. (2007). Gender differences in Parkinson's disease. J. Neurol. Neurosurg. Psychiatry 78, 819–824. doi: 10.1136/jnnp.2006.103788 PubMed Abstract | CrossRef Full Text | Google Scholar Hackney, M. E., and Earhart, G. M. (2008). Tai Chi improves balance and mobility in people with Parkinson disease. Gait Posture 28, 456–460. doi: 10.1016/j.gaitpost.2008.02.005 PubMed Abstract | CrossRef Full Text | Google Scholar Hackney, M. E., Kantorovich, S., Levin, R., and Earhart, G. M. (2007). Effects of tango on functional mobility in Parkinson's disease: a preliminary study. J. Neurol. Phys. Ther. 31, 173–179. doi: 10.1097/NPT.0b013e31815ce78b PubMed Abstract | CrossRef Full Text | Google Scholar Herman, T., Giladi, N., Gruendlinger, L., and Hausdorff, J. M. (2007). Six weeks of intensive treadmill training improves gait and quality of life in patients with Parkinson's disease: a pilot study. Arch. Phys. Med. Rehabil. 88, 1154–1158. doi: 10.1016/j.apmr.2007.05.015 PubMed Abstract | CrossRef Full Text | Google Scholar Inkster, L. M., Eng, J. J., MacIntyre, D. L., and Stoessl, A. J. (2003). Leg muscle strength is reduced in Parkinson's disease and relates to the ability to rise from a chair. Mov. Disord. 18, 157–162. doi: 10.1002/mds.10299 PubMed Abstract | CrossRef Full Text | Google Scholar Jacobs, P. L. (2018). NSCA's Essentials of Training Special Populations. Champaign, IL: Human Kinetics. Google Scholar Kim, J. S., Ryu, S. Y., Yun, I., Kim, W. J., Lee, K. S., Park, J. W., et al. (2006). 1α,25-dihydroxyvitamin D(3) protects dopaminergic neurons in rodent models of Parkinson's disease through inhibition of microglial activation. J. Clin. Neurol. 2, 252–257. doi: 10.3988/jcn.2006.2.4.252 PubMed Abstract | CrossRef Full Text | Google Scholar Kokko, S. M., Paltamaa, J., Ahola, E., and Mälkiä, E. (1997). The assessment of functional ability in patients with Parkinson's disease: the PLM-test and three clinical tests. Physiother. Res. Int. 2, 29–45. doi: 10.1002/pri.88 PubMed Abstract | CrossRef Full Text | Google Scholar Kones, R. (2010). Parkinson's disease: mitochondrial molecular pathology, inflammation, statins, and therapeutic neuroprotective nutrition. Nutr. Clin. Pract. 25, 371–389. doi: 10.1177/0884533610373932 PubMed Abstract | CrossRef Full Text | Google Scholar Liu, R., Baird, D., Park, Y., Freedman, N. D., Huang, X., Hollenbeck, A., et al. (2014). Female reproductive factors, menopausal hormone use, and Parkinson's disease. Mov. Disord. 29, 889–896. doi: 10.1002/mds.25771 PubMed Abstract | CrossRef Full Text | Google Scholar Lv, M., Zhang, Y., Chen, G. C., Li, G., Rui, Y., Qin, L., et al. (2017). Reproductive factors and risk of Parkinson's disease in women: a meta-analysis of observational studies. Behav. Brain Res. 335, 103–110. doi: 10.1016/j.bbr.2017.07.025 PubMed Abstract | CrossRef Full Text | Google Scholar Martinez-Martin, P., Falup Pecurariu, C., Odin, P., van Hilten, J. J., Antonini, A., Rojo-Abuin, J. M., et al. (2012). Gender-related differences in the burden of non-motor symptoms in Parkinson's disease. J. Neurol. 259, 1639–1647. doi: 10.1007/s00415-011-6392-3 PubMed Abstract | CrossRef Full Text | Google Scholar Miller, I. N., and Cronin-Golomb, A. (2011). Gender differences in Parkinson's disease: clinical characteristics and cognition. Mov. Disord. 25, 2695–2703. doi: 10.1002/mds.23388 PubMed Abstract | CrossRef Full Text | Google Scholar Moore, G. E., Durstine, J. L., and Painter, P. L. (2016). ACSM's Exercise Management for Persons with Chronic Diseases and Disabilities. Champaign, IL: Human Kinetics. Google Scholar Morley, J. E., Morris, J. C., Berg-Weger, M., Borson, S., Carpenter, B. D., Del Campo, N., et al. (2015). Brain health: the importance of recognizing cognitive impairment: an IAGG consensus conference. J. Am. Med. Dir. Assoc. 16, 731–739. doi: 10.1016/j.jamda.2015.06.017 PubMed Abstract | CrossRef Full Text | Google Scholar Nakamura, T., Hirayama, M., Hara, T., Hama, T., Watanabe, H., and Sobue, G. (2011). Does cardiovascular autonomic dysfunction contribute to fatigue in Parkinson's disease? Mov. Disord. 26, 1869–1874. doi: 10.1002/mds.23744 PubMed Abstract | CrossRef Full Text | Google Scholar National Institutes of Health. (2017). “Sex/Gender.” Available online at: https://orwh.od.nih.gov/research/sex-gender/ (Accessed Nov 19, 2017). Nicoletti, A., Nicoletti, G., Arabia, G., Annesi, G., De Mari, M., Lamberti, P., et al. (2011). Reproductive factors and Parkinson's disease: a multicenter case-control study. Mov. Disord. 26, 2563–2566. doi: 10.1002/mds.23951 PubMed Abstract | CrossRef Full Text | Google Scholar Pääsuke, M., Mottus, K., Ereline, J., Gapeyeva, H., and Taba, P. (2002). Lower limb performance in older female patients with Parkinson's disease. Aging Clin. Exp. Res. 14, 185–191. doi: 10.1007/BF03324434 PubMed Abstract | CrossRef Full Text | Google Scholar Pedersen, S. W., Oberg, B., Larsson, L. E., and Lindval, B. (1997). Gait analysis, isokinetic muscle strength measurement in patients with Parkinson's disease. Scand. J. Rehabil. Med. 29, 67–74. PubMed Abstract | Google Scholar Picillo, M., Amboni, M., Erro, R., Longo, K., Vitale, C., Moccia, M., et al. (2013). Gender differences in non-motor symptoms in early, drug naïve Parkinson's disease. J. Neurol. 260, 2849–2855. doi: 10.1007/s00415-013-7085-x PubMed Abstract | CrossRef Full Text | Google Scholar Pinter, B., Diem-Zangerl, A., Wenning, G. K., Scherfler, C., Oberaigner, W., Seppi, K., et al. (2015). Mortality in Parkinson's disease: a 38-year follow-up study. Mov. Disord. 30, 266–269. doi: 10.1002/mds.26060 PubMed Abstract | CrossRef Full Text | Google Scholar Ritz, B., Ascherio, A., Checkoway, H., Marder, K. S., Nelson, L. M., Rocca, W. A., et al. (2007). Pooled analysis of tobacco use and risk of Parkinson disease. Arch. Neurol. 64, 990–997. doi: 10.1001/archneur.64.7.990 PubMed Abstract | CrossRef Full Text | Google Scholar Rodriguez-Perez, A. I., Borrajo, A., Valenzuela, R., Lanciego, J. L., and Labandeira-Garcia, J. L. (2015). Critical period for dopaminergic neuroprotection by hormonal replacement in menopausal rats. Neurobiol. Aging 36, 1194–1208. doi: 10.1016/j.neurobiolaging.2014.10.028 PubMed Abstract | CrossRef Full Text | Google Scholar Sato, K., Hatano, T., Yamashiro, K., Kagohashi, M., Nishioka, K., Izawa, N., et al. (2006). Prognosis of Parkinson's disease: time to stage III, IV, V, and to motor fluctuations. Mov. Disord. 21, 1384–1395. doi: 10.1002/mds.20993 PubMed Abstract | CrossRef Full Text | Google Scholar Schenkman, M., Cutson, T. M., Kuchibhatla, M., Chandler, J., Pieper, C. F., Ray, L., et al. (1998). Exercise to improve spinal flexibility and function for people with Parkinson's disease: a randomized, controlled trial. J. Am. Geriatr. Soc. 46, 1207–1216. doi: 10.1111/j.1532-5415.1998.tb04535.x PubMed Abstract | CrossRef Full Text | Google Scholar Shulman, L. M. (2007). Gender differences in Parkinson's disease. Gend. Med. 4, 8–18. doi: 10.1016/S1550-8579(07)80003-9 PubMed Abstract | CrossRef Full Text | Google Scholar Shulman, L. M., Katzel, L. I., Ivey, F. M., Sorkin, J. D., Favors, K., and Anderson, K. E. (2013). Randomized clinical trial of 3 types of physical exercise for patients with Parkinson's disease. JAMA Neurol. 70, 183–190. doi: 10.1001/jamaneurol.2013.646 PubMed Abstract | CrossRef Full Text | Google Scholar Simon, K. C., Chen, H., Gao, X., Schwarzschild, M. A., and Ascherio, A. (2009). Reproductive factors, exogenous estrogen use, and risk of Parkinson's disease. Mov. Disord. 24, 1359–1365. doi: 10.1002/mds.22619 PubMed Abstract | CrossRef Full Text | Google Scholar Simunovic, F., Yi, M., Wang, Y., Stephens, R., and Sonntag, K. C. (2010). Evidence for gender-specific transcriptional profiles of nigral dopamine neurons in Parkinson disease. PLoS ONE 5:e8856. doi: 10.1371/journal.pone.0008856 PubMed Abstract | CrossRef Full Text | Google Scholar Smith, M. P., Fletcher-Turner, A., Yurek, D. M., and Cass, W. A. (2006). Calcitriol protection against dopamine loss induced by intracerebroventricular administration of 6-hydroxydopamine. Neurochem. Res. 31, 533–539. doi: 10.1007/s11064-006-9048-4 PubMed Abstract | CrossRef Full Text | Google Scholar Solla, P., Cannas, A., Ibba, F. C., Loi, F., Corona, M., Orofino, G., et al. (2012). Gender differences in motor and non-motor symptoms among Sardinian patients with Parkinson's disease. J. Neurol. Sci. 323, 33–39. doi: 10.1016/j.jns.2012.07.026 PubMed Abstract | CrossRef Full Text | Google Scholar Szewczyk-Krolikowski, K., Tomlinson, P., Nithi, K., Wade-Martinis, R., Talbot, K., Ben-Shlomo, Y., et al. (2014). The influence of age and gender on motor and non-motor features of early Parkinson's disease: initial findings from the Oxford Parkinson Disease Center (OPDC) discovery cohort. Parkinsonism Relat. Disord. 20, 99–105. doi: 10.1016/j.parkreldis.2013.09.025 PubMed Abstract | CrossRef Full Text | Google Scholar Taylor, K. S., Cook, J. A., and Counsell, C. E. (2007). Heterogeneity in male to female risk for Parkinson's disease. J. Neurol. Neurosurg. Psychiatry 78, 905–906. doi: 10.1136/jnnp.2006.104695 PubMed Abstract | CrossRef Full Text | Google Scholar Winter, A. C., Rist, P. M., Buring, J. E., and Kurth, T. (2016). Prospective comorbidity-matched study of Parkinson's disease and risk of mortality among women. BMJ Open 6:e011888. doi: 10.1136/bmjopen-2016-011888 PubMed Abstract | CrossRef Full Text | Google Scholar Xu, J., Gong, D. D., Man, C. F., and Fan, Y. (2014). Parkinson's disease and risk of mortality: meta-analysis and systematic review. Acta. Neurol. Scand. 129, 71–79. doi: 10.1111/ane.12201 PubMed Abstract | CrossRef Full Text | Google Scholar Ziemssen, T., and Reichmann, H. (2010). Cardiovascular autonomic dysfunction in Parkinson's disease. J. Neurol. Sci. 289, 74–80. doi: 10.1016/j.jns.2009.08.031 PubMed Abstract | CrossRef Full Text | Google Scholar Keywords: aerobic, female, flexibility, functional abilities, neurodegenerative, resistance Citation: Rigby BR and Davis RW (2018) Should Exercise Be Prescribed Differently Between Women and Men? An Emphasis on Women Diagnosed With Parkinson's Disease. Front. Physiol. 9:1040. doi: 10.3389/fphys.2018.01040 Received: 30 March 2018; Accepted: 12 July 2018; Published: 02 August 2018. Edited by: Reviewed by: Copyright © 2018 Rigby and Davis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Brandon R. Rigby, [email protected]
, Yannis Jaquet, Djilani J. Kebaili, Laura Symul,
Published: 4 July 2018
Frontiers in Nutrition, Volume 5; https://doi.org/10.3389/fnut.2018.00057

Abstract:
Metabolic disorders, such as diabetes or obesity, have become a major public health concern, with increasingly large parts of the global population affected (1, 2). Nutritional epidemiologists hope to better understand the underlying causes, the potential treatments and prevention strategies by analyzing population and individual patterns through studies that generally rely on surveying dietary habits. Traditional food-intake survey methods are based on questionnaires filled by participants at a given frequency. The frequency of diet records is an important factor contributing to the accuracy of the study (3). Multiple-day diet records might provide good accuracy when not based on memory, but require strong motivation and time commitment by the participants. Approaches like multiple/single 24-h recalls—involving a specialized interviewer performing surveys in person or on the phone with the participants—require less engagement, but pose issues with missing data as they rely on short-term memory. Finally, so-called Food Frequency Questionnaires, where participants are asked to indicate the frequency of intake of certain foods over long periods of time (typically 1 year), demand minimal participants' commitment, therefore allowing for large cohort studies on long-term dietary habits. However, the likelihood of missing or incorrect data increases as they count on participants' long-term memory. Overall, self-reported dietary data present biases which limit their applications, especially when they heavily rely on participants' memory (4). Such limitations, which should be properly addressed in further epidemiological studies, may be overcome with more advanced recording methodologies such as dietary biomarkers and digital technologies (5). Recent technological advances, and in particular the emergence and almost complete market penetration of smartphones, have offered interesting surveying alternatives. In particular, mobile phones have been successfully deployed in several food-related studies (6), for example using food photography (7–12). Other research has also explored the possibility of recording dietary habits by asking participants to scan the barcodes of their consumed food (13, 14). Although further investigations are required to assess self-reporting biases, these advances in nutritional research have triggered the release of mobile apps oriented mainly toward diabetes and weight-loss self-management (15–19), showing the willingness and interest of users to monitor their food intake if it provides potential health benefits. The further expansion of self-monitoring for research and medical purposes relies on comprehensive and continuously updated food databases. A few databases of barcoded products already exist, for example Open Food Facts (20) or the USDA Food Composition Databases (21). While they each have their strength, not all of them are openly accessible or, and they often have a limited product coverage, and are often not regularly updated. For Switzerland, we did not find any database whose product coverage was sufficiently high, where the data was completely open, and easily accessible through an Application Programming Interface (API). The last point was particularly important to us, as APIs are necessary for third parties to dynamically use the data in their products and services. Our approach was therefore to build an openly accessible database of barcoded food products with sufficiently high coverage, accessible through a stable API. Rather than focusing on a wide geographic range, we focused on a small country (Switzerland) in order to obtain the necessary coverage. The focus on the Swiss market further benefits from the need to support multiple languages from the beginning, thus making the system readily expandable to other countries, which we are now planning to do. Here, we present this system, which we call FoodRepo (https://www.foodrepo.org), an openly accessible database of barcoded food products, and we describe the data-acquisition framework, its quality control and maintenance. Here, the word repository is meant to be understood as a data repository, where the community can deposit an increasing number of datapoints on food products. The growing community around FoodRepo and the validation of new products make our database robust, scalable and self-sustainable in the long run. Currently, the FoodRepo database mostly holds products sold in Switzerland, from the main grocery stores in the country. Its international expansion is under development. Any item in the database is accessible through the FoodRepo website (for an example of products contained in the FoodRepo database, please see Figure 1A) or via our API, described in section Usage Notes. The CC-BY-4 license under which our database is released will allow its exploitation by different type of users, from academic researchers to commercial partners. For instance, a Swiss consumers association is using FoodRepo data in their NutriScan mobile app (22) to make the food package information more accessible, and to provide their users with an overall nutritional score. Figure 1. (A) Screenshot from the webpage of a product on the FoodRepo website. (B) Schematic representation of the pipeline behind our API. When a user or an application (left column) sends a call to the API, the request is handled by the server that hosts the API (middle column). This sends then a query to the server which hosts the FoodRepo database (right column), where the query is handled by the Elastic Search engine. The data is returned to the API server which performs final formatting before giving it back to the user or the application. (C) Distribution of API response times, color-coded according to different sections of the back-end pipeline, as shown in (B). In green (main plot and inset) the response-times of the Elastic Search server to the application server; in blue the full time needed for a user to have the data after a call to our API. Beyond this specific example, the FoodRepo database opens the way for promising research opportunities in the field of digital epidemiology and personalized nutrition. Notably, we foresee that, through dietary live-tracking, this database can support studies which combine other recent technological developments and new findings in our understanding of the human metabolism. For example, phone-connected devices for continuous monitoring of blood glucose levels have recently been made available to diabetic patients (23, 24), as well as numerous direct-to-consumer devices to estimate glucose levels have appeared on the market. A plethora of other wireless sensors are now also available to record various physiological parameters such as heart rate or blood pressure, marking a new era of “high-throughput human phenotyping” (25). Studies that would simultaneously track participants' parameters, food intake, glycemic response and physical activity might provide detailed insights on the variability of individual metabolic responses. Interestingly, one of the factors which has recently been found to account for a large part of this variability is microbiota (26–30). Large-scale testing of these hypotheses through self-tracking could contribute to the assessment of the complex metabolic response of the human body to different energy sources. This requires detailed records of food intake that includes nutritional information as well as eating times (31) and food portion sizes (32–34), all challenges that FoodRepo may help to overcome. However, we highlight an important limitation of all food databases. Generally, the curators of such repositories cannot ensure the validity of the data reported by the producers on the nutrition facts labels. It is indeed well known in the literature that there might be large discrepancies between the reported nutrients and the actual food content, due to different factors, such as food pre-processing or the different industry standards (35–40). Therefore, all studies using databases such as the one presented here would do well to assess the validity of such data and ideally quantify the reporting errors, especially when using the reported data on nutritional values. Analyses of the database evolution will give interesting indication on the dietary trends and on the overall modification of the nutritive quality of packaged food. Although the database itself does not inform on the buying frequency, the continuous introduction of specific products in the market and thus in the database can potentially indicate how retailers react to customer demands and changing dietary habits. The database building and maintenance process relies on the following steps: (i) collection of product pictures from local retailers, (ii) data extraction from the pictures, (iii) validation of the extracted data, and (iv) permanent storage in the database (Figure 2). For the initial build of the database, we designed a specific pipeline (bootstrap workflow, Figure 2A, which allowed us to validate the first 20,000 food products in a few months. Given the dynamic nature of our data and the cost of the bootstrap workflow, we designed a second pipeline (currently under development) which relies on the growing FoodRepo community. This workflow (community-based, Figure 2B) allows us to keep up with the new and seasonal products introduced to the market by the retail shops, as well as to ensure the scalability and self-sustainability of FoodRepo in the long run. Figure 2. Schematic overview of FoodRepo data collection and validation processes. The two workflows are illustrated here. The bootstrap workflow (A) was based on the joint work of the FoodRepo team and crowd-sourced workers collecting and validating the data. This allowed the storage of the first 14,000 or so products in the database. The community-based workflow (B) allows for long-term sustainability of the database thanks to customers uploading new products through FoodRepo mobile app and the continuous support of the FoodRepo team. The bootstrap workflow (Figure 2A) consists of 3 main steps. The first step entailed a massive manual data collection from three large groceries stores in Switzerland upon approval from the shops (specifically Migros, Coop, and Lidl). We hired students to take pictures of all barcoded food items in retail shops located in the Lausanne area. To facilitate the data collection, we specifically designed a simple phone app with which students could scan the products' barcode and take pictures of the front and back of the package, the product's name, ingredients list, and nutrition facts. These pictures were then automatically uploaded to the database. At the end of this step, students had collected on average 4.4 pictures per item. The second step focused on the extraction of information contained in the pictures. Due to the presence of multi-language ingredients and the often wrinkled surfaces of item packaging, Optical Character Recognition (OCR) systems could not achieve a reliable accuracy. We therefore opted for a crowd-sourced solution and in particular we decided to recruit workers on Amazon Mechanical Turk (41) (AMT). AMT is a platform connecting requesters to workers, the latter being financially compensated to achieve tasks requiring human intelligence (HITs—Human Intelligence Tasks). Here, we designed a graphical user interface (GUI) allowing workers to transcribe the text they could read from product pictures. Specifically, the GUI presented text boxes where AMT workers provided the product name, nutritional values (in a table format) and ingredients, in every language present on the label (German and/or French for almost all items; Italian and/or English in addition for some products). Three different HITs were set up: one for nutrients, one for product name and one for ingredients. For the last two, we set up qualification rounds for AMT workers as their transcription involved some language skills. AMT workers could choose to either enter from scratch the information they saw on the pictures, or to approve/modify the suggestions given by an OCR (42) system. At the end of the second step, all annotated products were uploaded into the database, flagged as ready for validation. The third step was thus dedicated to data validation, which was based on extensive manual checking by the FoodRepo team, and was additionally informed by manual reports from visitors to the FoodRepo website and with error-detection analyses of nutritional values. Such online reports are encouraged by the presence of a “report an issue” button on each product web-page, which prompts a visitor to file an issue when spotting a potential error. Details about the error-detection analyses are given in the Technical Validation section. Before the final validation of the data, the FoodRepo team as well as students manually checked all products thoroughly. The community-based workflow (Figure 2B) is similar to the bootstrap workflow, but instead of counting on AMT workers, it relies on the growing FoodRepo community. As new products become available in retail shops, FoodRepo users can submit them by uploading the corresponding package pictures, using the FoodRepo smartphone app. Currently, the information extraction is still performed by the FoodRepo team, but additional features are being implemented in the app, which will allow users to directly type the product details contained on the package. Before user-provided information is permanently stored in the FoodRepo database, consistent entries will need to be submitted by at least three different FoodRepo users. If such consensus will not be reached after seven independent submissions (i.e., there are still less than three consistent entries), the item will be manually analyzed by the FoodRepo team for definitive validation and inclusion into the database. This procedure will ensure minimal intervention from our team, while still guaranteeing the reliability of the data. The FoodRepo team is currently fostering the development of an active community through which the continuity of FoodRepo is assured, and which will likely accelerate the birth of independent exploitations of the database, from both public and private partners. All FoodRepo data are stored in a PostgreSQL (43) database, physically hosted on a server in Ireland. For a quick overview of the dataset, a database dump can be downloaded from the dedicated folder in our API repository (44). However, these dumps are not generated regularly, and we strongly encourage the use of the API which delivers up-to-date information. For each product, which comes with a unique numerical identifier, the database contains pictures of the item as found in the shop (usually between three to seven .jpg files), together with the main information presented on the package, i.e., the product name, nutritional values, ingredients list, barcode, and country of origin. The database holds as well the dates of the creation and last modification of the related item in the database (see Table 1). The programmatic access to the database is allowed by an API, described in the section Usage Notes. Table 1. Sample product from the FoodRepo database with its values for the most relevant fields. As described in the Methods section, during the bootstrap stage (Figure 2A) the final validation was performed manually by the FoodRepo team, while in the community workflow (Figure 2B), the accuracy of the data is ensured by the consensus test (the FoodRepo team intervenes only if fewer than three matches are achieved after the uploads of the same product by seven different users). We highlight here that FoodRepo strictly reflects the information printed on products packages, even when suspicious values are present on the labels. All validation processes have thus been set-up to detect transcription errors. Within this rationale, computational analyses were implemented for the detection of outliers, in particular regarding the nutritional values. These tests reflect basic constraints, such as the mass upper-limit: where p, f, c are respectively the product's protein, fat and carbohydrates concentrations expressed in grams per 100 g of product. From Equation (1), one can also derive other linear inequalities for a single nutrient or couples of nutrients, namely p + f ≤ 100, p + c ≤ 100, and c + f ≤ 100. These simple tests allowed us to detect transcription errors in earlier versions of the database, as illustrated by the outliers in Figure 3A which shows the distribution of products in the fat-carbohydrates space with the joint mass boundary. Figure 3. Examples of tests implemented with linear boundaries on nutritional values. Dots outside the boundaries have been inspected and corrected whenever data were different from the products packages. Products in the fat/carbohydrates concentrations space (A), saturated fat/fat concentrations space (B) and energy density/fat concentration space (C). Similarly, other typos could be spotted by checking that the concentration of a subclass of nutrient is smaller than the one of the parent-class. This is the case for instance of sugars vs. carbohydrates, or saturated-fat vs. fat, shown in Figure 3B. Another simple relation that helps check products' nutrition facts can be derived from the standard approximation of energy density based on nutrients composition (45): where the product's energy content E is expressed in kCal/100 g. Combining expressions 1 and 2 provides upper and lower boundaries for the energy content (for example Figure 3C). In this case however, not all dots that fall outside the boundaries were due to typos in transcription. Indeed, the approximation in Equation (2) does not take into account the different contribution to energy of complex carbohydrates such as polyols, which account for less than 4 kCal/g. This is why products such as candies and chewing gums would fall below the energy boundaries. In order to facilitate the access to the database, we built an openly accessible API. Any terminal user, including third party apps or services, can send API requests to retrieve specific data. The API pipeline is illustrated in Figure 1B. User's requests are handled on an application server, where an Elastic Search (ES) application handles the queries on another cloud computing service, based in Ireland. The ES response is then returned to the user after JSON formatting and compression (on demand). We checked that handling the request between the two servers does not critically compromise the total user-response time. We run series of single-page API calls, every 6 h, over a week, in order to measure the full response-time and the application server response-time. We observed that the latter was consistently fast across all experiments (in the range of 20–50 ms) and that the bottleneck was rather the transmission between the terminal user and the application server (the average full response time was about 250 ms—see Figure 1C). For a quick introduction to the API endpoints, users are welcome to try them out on the API Playground page (46). Furthermore, on the project's GitHub repository, one can also find usage cases (47) in Python, Ruby, Curl and JavaScript, as well as examples of complex queries which include fuzzy searches (48). When fetching a large amount of data, we suggest using the option of compressed data1 and the possibility to include/exclude specific fields of each product [see for details the API documentation (46)]. In this way, one could reduce the response payload size by up to a factor of 10. We remind readers that all contents (other than computer software) made available by FoodRepo on its websites, apps or services are licensed under the Creative Commons Attribution 4.0 International License. We however would like to highlight the fact that product images may contain copyrighted data such as brand logos. GL performed the descriptive and validation analysis of the dataset. YJ built the FoodRepo database, website, API and AMT HITs. DK maintained the API, coordinated the manual data validation and built the framework for the FoodRepo community. GL, LS, and MS wrote the manuscript. MS initiated and supervised the project. This project was supported by a grant from the Jebsen foundation (49). The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. We are grateful to Migros, Coop, and Lidl for access to their retail shops. • API: Application Programming Interface—an set of tools and methods that allow to types of software to communicate. The FoodRepo API allows other applications to get and use the data. • CC-BY-4: Creative-Commons public license, with the “Attribution” term. It implies that anyone is free to share and transform the content of FoodRepo, even for commercial purposes, with the obligation to properly give credit to FoodRepo, and to display any modification without claiming direct endorsement from FoodRepo. For a detailed description, see the license text at https://creativecommons.org/licenses/by/4.0/ • OCR: Optical Character Recognition—tools that allow for automatic conversion of text contained in images to machine-readable formats. • AMT: Amazon Mechanical Turk—web platform providing a marketplace, where workers perform tasks set up by requesters, usually in exchange of money. • HIT: Human Intelligence Task—task related performed by workers in crowd-sourcing platform, such as AMT. • PostgreSQL: A popular and freely available relational database. • JSON: a JavaScript-based file format commonly used for browser-server data exchange. • Elastic Search: a very popular open-source search-engine. 1. ^This can be done by simply setting in the request header: Accept-Encoding: gzip 1. WHO. Diabetes. Available online at: http://www.who.int/mediacentre/factsheets/fs312/en/. 2. WHO. Obesity and Overweight. Available online at: http://www.who.int/mediacentre/factsheets/fs311/en/. 3. Satija A, Yu E, Willett WC, Hu FB. Understanding nutritional epidemiology and its role in policy. Adv Nutr Int Rev J. (2015) 6:5–18. doi: 10.3945/an.114.007492 PubMed Abstract | CrossRef Full Text | Google Scholar 4. Archer E, Pavela G, Lavie CJ. The inadmissibility of what we eat in America and NHANES dietary data in nutrition and obesity research and the scientific formulation of national dietary guidelines. Mayo Clin Proc. (2015) 90:911–26. doi: 10.1016/j.mayocp.2015.04.009 PubMed Abstract | CrossRef Full Text | Google Scholar 5. Subar AF, Freedman LS, Tooze JA, Kirkpatrick SI, Boushey C, Neuhouser ML, et al. Addressing current criticism regarding the value of self-report dietary data, 2. J Nutr. (2015) 145:2639–45. doi: 10.3945/jn.115.219634 CrossRef Full Text | Google Scholar 6. Sharp DB, Allman-Farinelli M. Feasibility and validity of mobile phones to assess dietary intake. Nutrition (2014) 30:1257–66. doi: 10.1016/j.nut.2014.02.020 PubMed Abstract | CrossRef Full Text | Google Scholar 7. Chae J, Woo I, Kim S, Maciejewski R, Zhu F, Delp EJ, et al. Volume estimation using food specific shape templates in mobile image-based dietary assessment. In: Proceedings of SPIE. Vol. 7873. NIH Public Access. San Francisco, CA (2011). p. 78730K. PubMed Abstract | Google Scholar 8. Kong F, Tan J. DietCam: automatic dietary assessment with mobile camera phones. Pervas Mob Comput. (2012) 8:147–63. doi: 10.1016/j.pmcj.2011.07.003 CrossRef Full Text | Google Scholar 9. Lee CD, Chae J, Schap TE, Kerr DA, Delp EJ, Ebert DS, et al. Comparison of known food weights with image-based portion-size automated estimation and adolescents' self-reported portion size. J Diab Sci Technol. (2012) 6:428–34. doi: 10.1177/193229681200600231 PubMed Abstract | CrossRef Full Text | Google Scholar 10. Dibiano R, Gunturk BK, Martin CK. Food image analysis for measuring food intake in free living conditions. In: Medical Imaging: Image Processing. Orlando, FL (2013). p. 86693N. Google Scholar 11. Zhu F, Bosch M, Khanna N, Boushey CJ, Delp EJ. Multilevel segmentation for food classification in dietary assessment. In: Image and Signal Processing and Analysis (ISPA), 2011 7th International Symposium on. Dubrovnik: IEEE (2011). p. 337–42. PubMed Abstract | Google Scholar 12. Zhu F, Bosch M, Woo I, Kim S, Boushey CJ, Ebert DS, et al. The use of mobile devices in aiding dietary assessment and evaluation. IEEE J Select Top Signal Proces. (2010) 4:756–66. doi: 10.1109/JSTSP.2010.2051471 PubMed Abstract | CrossRef Full Text | Google Scholar 13. Siek KA, Connelly KH, Rogers Y, Rohwer P, Lambert D, Welch JL. When do we eat? An evaluation of food items input into an electronic food monitoring application. In: Pervasive Health Conference and Workshops, 2006. Innsbruck: IEEE (2006). p. 1–10. Google Scholar 14. Eyles H, Jiang Y, Mhurchu CN. Use of household supermarket sales data to estimate nutrient intakes: a comparison with repeat 24-hour dietary recalls. J Am Diet Assoc. (2010) 110:106–10. doi: 10.1016/j.jada.2009.10.005 PubMed Abstract | CrossRef Full Text | Google Scholar 15. Pagoto S, Schneider K, Jojic M, DeBiasse M, Mann D. Evidence-based strategies in weight-loss mobile apps. Am J Prevent Med. (2013) 45:576–82. doi: 10.1016/j.amepre.2013.04.025 PubMed Abstract | CrossRef Full Text | Google Scholar 16. Dunford E, Trevena H, Goodsell C, Ng KH, Webster J, Millis A, et al. FoodSwitch: a mobile phone app to enable consumers to make healthier food choices and crowdsourcing of national food composition data. JMIR mHealth uHealth. (2014) 2:e37. doi: 10.2196/mhealth.3230 PubMed Abstract | CrossRef Full Text | Google Scholar 17. Stephens J, Allen JK, Himmelfarb CRD. “Smart” coaching to promote physical activity, diet change, and cardiovascular health. J Cardiovasc Nurs. (2011) 26:282. doi: 10.1097/JCN.0b013e31821ddd76 PubMed Abstract | CrossRef Full Text | Google Scholar 18. Tsai CC, Lee G, Raab F, Norman GJ, Sohn T, Griswold WG, et al. Usability and feasibility of PmEB: a mobile phone application for monitoring real time caloric balance. Mob Netw Appl. (2007) 12:173–84. doi: 10.1007/s11036-007-0014-4 CrossRef Full Text | Google Scholar 19. Azar KM, Lesser LI, Laing BY, Stephens J, Aurora MS, Burke LE, et al. Mobile applications for weight management: theory-based content analysis. Am J Prev Med. (2013) 45:583–9. doi: 10.1016/j.amepre.2013.07.005 PubMed Abstract | CrossRef Full Text | Google Scholar 20. Open Food Facts. Available online at: https://world.openfoodfacts.org/. 21. USDA Food Composition Database. Available online at: https://ndb.nal.usda.gov/ndb/. 22. Application NutriScan. Available online at: https://www.bonasavoir.ch/nutriscan. 23. Pfeiffer E. The glucose sensor: the missing link in diabetes therapy. Horm Metab Res Suppl Ser. (1989) 24:154–64. PubMed Abstract | Google Scholar 24. Aljasem LI, Peyrot M, Wissow L, Rubin RR. The impact of barriers and self-efficacy on self-care behaviors in type 2 diabetes. Diab Educ. (2001) 27:393–404. doi: 10.1177/014572170102700309 PubMed Abstract | CrossRef Full Text | Google Scholar 25. Elenko E, Underwood L, Zohar D. Defining digital medicine. Nat Biotechnol. (2015) 33:456–61. doi: 10.1038/nbt.3222 PubMed Abstract | CrossRef Full Text | Google Scholar 26. Griffin NW, Ahern PP, Cheng J, Heath AC, Ilkayeva O, Newgard CB, et al. Prior dietary practices and connections to a human gut microbial metacommunity alter responses to diet interventions. Cell Host Microbe. (2017) 21:84–96. doi: 10.1016/j.chom.2016.12.006 PubMed Abstract | CrossRef Full Text | Google Scholar 27. Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature (2006) 444:1027–31. doi: 10.1038/nature05414 PubMed Abstract | CrossRef Full Text | Google Scholar 28. Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, et al. Richness of human gut microbiome correlates with metabolic markers. Nature (2013) 500:541–6. doi: 10.1038/nature12506 PubMed Abstract | CrossRef Full Text | Google Scholar 29. Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, et al. Personalized nutrition by prediction of glycemic responses. Cell (2015) 163:1079–94. doi: 10.1016/j.cell.2015.11.001 PubMed Abstract | CrossRef Full Text | Google Scholar 30. Pedersen HK, Gudmundsdottir V, Nielsen HB, Hyotylainen T, Nielsen T, Jensen BA, et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature (2016) 535:376–81. doi: 10.1038/nature18646 PubMed Abstract | CrossRef Full Text | Google Scholar 31. Scheer FA, Hilton MF, Mantzoros CS, Shea SA. Adverse metabolic and cardiovascular consequences of circadian misalignment. Proc Natl Acad Sci USA. (2009) 106:4453–8. doi: 10.1073/pnas.0808180106 PubMed Abstract | CrossRef Full Text | Google Scholar 32. Ello-Martin JA, Ledikwe JH, Rolls BJ. The influence of food portion size and energy density on energy intake: implications for weight management. Am J Clin Nutr. (2005) 82:236S–41S. doi: 10.1093/ajcn/82.1.236S PubMed Abstract | CrossRef Full Text | Google Scholar 33. Ledikwe JH, Ello-Martin JA, Rolls BJ. Portion sizes and the obesity epidemic. J Nutr. (2005) 135:905–9. doi: 10.1093/jn/135.4.905 PubMed Abstract | CrossRef Full Text | Google Scholar 34. Young LR, Nestle M. The contribution of expanding portion sizes to the US obesity epidemic. Am J Public Health (2002) 92:246–9. doi: 10.2105/AJPH.92.2.246 PubMed Abstract | CrossRef Full Text | Google Scholar 35. Ng SW, Popkin BM. Monitoring foods and nutrients sold and consumed in the United States: dynamics and challenges. J Acad Nutr Diet. (2012) 112:41–5. doi: 10.1016/j.jada.2011.09.015 PubMed Abstract | CrossRef Full Text | Google Scholar 36. Ng S, Dunford E. Complexities and opportunities in monitoring and evaluating US and global changes by the food industry. Obes Rev. (2013) 14:29–41. doi: 10.1111/obr.12095 PubMed Abstract | CrossRef Full Text | Google Scholar 37. Ahuja JK, Lemar L, Goldman JD, Moshfegh AJ. The impact of revising fats and oils data in the US Food and Nutrient Database for Dietary Studies. J Food Compos Anal. (2009) 22:S63–7. doi: 10.1016/j.jfca.2009.02.005 CrossRef Full Text | Google Scholar 38. Merchant AT, Dehghan M. Food composition database development for between country comparisons. Nutr J. (2006) 5:2. doi: 10.1186/1475-2891-5-2 PubMed Abstract | CrossRef Full Text | Google Scholar 39. Phillips KM, Patterson KY, Rasor AS, Exler J, Haytowitz DB, Holden JM, et al. Quality-control materials in the USDA national food and nutrient analysis program (NFNAP). Anal Bioanal Chem. (2006) 384:1341–55. doi: 10.1007/s00216-005-0294-0 PubMed Abstract | CrossRef Full Text | Google Scholar 40. Deharveng G, Charrondiere U, Slimani N, Southgate D, Riboli E. Comparison of nutrients in the food composition tables available in the nine European countries participating in EPIC. Eur J Clin Nutr. (1999) 53:60. doi: 10.1038/sj.ejcn.1600677 PubMed Abstract | CrossRef Full Text | Google Scholar 41. Amazon Mechanical Turk. Available online at: https://www.mturk.com/. 42. Text, Recognition API Overview,. Google Developers. Available online at: https://developers.google.com/vision/text-overview. 43. PostgreSQL. The World's Most Advanced Open Source Database. Available online at: https://www.postgresql.org/. 44. FoodRepo Database Dumps. Available online at: https://github.com/salathegroup/foodrepo_api/tree/master/data. 45. COUNCIL DIRECTIVE of 24 September 1990 on Nutrition Labelling for Foodstuffs. Available online at: http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CONSLEG:1990L0496:20081211:EN:PDF. 46. OpenFood API Documentation. Available online at: https://www.foodrepo.org/api-docs/swaggers/v3. 47. OpenFood API GitHub Repository. Available online at: https://github.com/salathegroup/foodrepo_api/tree/master/v3/code. 48. Elasticsearch Queries Example. Available online at: https://github.com/salathegroup/foodrepo_api/blob/master/v3/code/meta/es_sample_queries_product.md. 49. Kristian Gerhard Jebsen Foundation. Available online at: http://www.kgjf.org/. Keywords: open data, digital health, nutrition, API, digital epidemiology Citation: Lazzari G, Jaquet Y, Kebaili DJ, Symul L and Salathé M (2018) FoodRepo: An Open Food Repository of Barcoded Food Products. Front. Nutr. 5:57. doi: 10.3389/fnut.2018.00057 Received: 05 March 2018; Accepted: 12 June 2018; Published: 04 July 2018. Edited by: Reviewed by: Copyright © 2018 Lazzari, Jaquet, Kebaili, Symul and Salathé. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Marcel Salathé, [email protected]
Journal of Christian Nursing, Volume 35, pp 11-11; https://doi.org/10.1097/cnj.0000000000000471

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We've expanded the Resources column where you found books, website reviews, and Going Deeper into more Bible study and discussion questions. We've given more space to delve deeper into select articles from this issue of JCN and dig into God's Word. The new Chat Room can be found on page 65. Chat Room is a great resource for your NCF meetings, whether for students, nurses, or faculty! Look at the end of each feature article in the Web Resources box to find additional content related to the article. Next issue, we will add books to the Web Resources list found with our feature articles. In September 2017, Nurses Christian Fellowship USA started Member Video Chats—a gathering of NCF USA members, for fellowship, discussion, and prayer via Zoom technology. The first chat focused on “Jesus, remember me,” the words of the dying criminal at the Crucifixion (Mark 15; Luke 23). In October, former NCF director Jane Hall led a discussion based on the JCN article Effective Listening: Five Lessons from the Best (Vol. 34, No. 3, pp. 159-163). In November we discussed professional and biblical responses to moral distress. NCF Member Video Chats are announced in the members-only eNewsletter Charting the Way and in special member emails. The chats and technology are free, and a Journal Club feature with 1 hour of free CE credit (ANCC approved) will be offered four times a year beginning in 2018! If you are an NCF USA member, watch for Video Chat information and join us! Not a member? Join NCF at http://ncf-jcn.org/membership. We are looking for quality, continuing education feature articles from a Christian worldview on these topics: Substance Abuse—Christian response to the opioid crisis, pain management of noncancer chronic pain (appropriate opioid use) Global Health Issues—Zika, spread of communicable diseases; Lyme Disease; nurses' roles/support; taking nursing students abroad Update on HIV/AIDS Inter-professional Collaboration Bio-Terrorism/Disaster Preparedness Infection Control Update Changing the Culture of Mistakes in the Workplace CE articles must be well documented, with current references; 4,900-5,600 words in length, including references, tables, and figures; case studies enhance content. Include additional content, slides, or videos beyond 5,600 words as supplemental digital content. Submit your article at http://www.edmgr.com/NCF-JCN/default.aspx JCN, a publication of Nurses Christian Fellowship USA, reaches around the globe. NCF USA is a member country of Nurses Christian Fellowship International (NCFI), which has over 40 member countries in six regions around the world! Read JCN's newest column—NCFI: Your Global Connection on page 10 to meet the NCFI president and learn more about NCFI. These and other events of interest can be found at www.journalofchristiannursing.com under Save the Dates and at Nurses Christian Fellowship's Events page http://ncf-jcn.org/resources/events Indiana Wesleyan University will hold its biannual Innovations in Faith-Based Nursing Conference, June 18-21, 2018, on the campus of IWU in Marion, Indiana. Find information at https://www.indwes.edu/events/nursing-innovations/ The Nurses Christian Fellowship International Caribbean and North America Quadrennial Regional Conference, Infusing Hope in Nursing: A Christian Perspective will be held on July 19-22, 2018, at Azusa Pacific University, California, USA. Information posted at http://ncfi.
Journal of Communications and Networks, Volume 19, pp 542-542; https://doi.org/10.1109/jcn.2017.000088

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Journal of Communications and Networks (JCN) membership application.
Journal of Communications and Networks, Volume 19, pp 1-5; https://doi.org/10.1109/jcn.2017.000110

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This index covers all papers that appeared in JCN during 2017. The Author Index contains the primary entry for each item, listed under the first author’s name, and cross-references from all coauthors. The Title Index contains paper titles for each Division in the alphabetical order from No. 1 to No. 6. Please refer to the primary entry in the Author Index for the exact title, coauthors, and comments / corrections.
Journal of Communications and Networks, Volume 18; https://doi.org/10.1109/jcn.2016.000097

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Provides a listing of the editorial board, current staff, committee members and society officers.
Frontiers in Cardiovascular Medicine, Volume 3; https://doi.org/10.3389/fcvm.2016.00011

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Cascade screening is one of the more forceful demonstrations that molecular biology and genetics are not just a tool for researchers, but represent an important and by now essential component of good medical care. – Peter J. Schwartz (1) There is much attention and excitement in the current health care environment on the potential of precision medicine based on a patient’s genomic data. Today, what arguably remains as one of the most valuable and informative genetic tests is that of predictive testing for a known familial pathogenic variant. Predictive genetic testing determines whether the pathogenic variant previously identified in an affected family member(s) is present or not in relatives at risk. Previous research has documented that affected individuals undergoing genetic testing cite obtaining genetic information for others as being the most important, if not the only, motivation for undergoing genetic testing (2). Predictive, cascade testing is able to separate at-risk relatives who require vigilant serial screening from those who do not. For those with the predisposition, clinical screening allows for early identification of the family’s phenotype, which when present, may require lifelong medical therapy, implantation of devices, and/or other types of medical management. Relatives who test negative for the familial variant can typically be released from lifelong screening. In addition, it is also then known that their children are not at increased risk for the family’s disease. This approach can save the health care system, and the family itself, thousands of dollars. Cascade screening is imperative with “high-stakes” cardiovascular conditions, such as familial hypercholesterolemia (FH), long QT syndrome (LQTS), and other inherited arrhythmias, as well as other heritable cardiovascular phenotypes, including cardiomyopathies and aneurysms, where there is an increased risk for sudden cardiac death and severe morbidities such as heart failure. The value of cascade screening for highly penetrant cardiovascular (and cancer) phenotypes has been acknowledged by public health officials. The United States Centers for Disease Control Office of Public Health Genomics classifies cascade screening of at-risk relatives for certain conditions, FH being one, as a Tier 1 genomic application, meaning it meets the criteria for analytic and clinical validity and utility and therefore has evidence supporting its implementation into practice (3). Cascade screening may include targeted genetic testing as well as clinical screening (e.g., lipid panel) of at-risk relatives. This opinion provides a brief summary of research in this area and poses questions to facilitate future discussion regarding the potential for direct contact of at-risk relatives. As a practicing genetic counselor in clinical genetic medicine for 14 years who has provided genetic counseling and testing to thousands of families with heritable cardiovascular and cancer conditions, it is my opinion that more could be done to provide assistance to probands for at-risk relative notification and that genetic counselors are in the ideal position to facilitate cascade testing and lead forward-thinking research in this area (4). Cascade screening is a mechanism for identifying people at risk for a genetic condition by a process of systematic family tracing. It should begin with first-degree relatives (parents, siblings, and children) and then extend to second- and third-degree relatives in a stepwise, cascade fashion, moving through the pedigree in sequential steps as additional family members are diagnosed until all at-risk relatives have been screened (5). Cascade screening for FH is a cost-effective method for identifying new cases of FH (6–8). Cascade screening in families with inherited arrhythmia syndromes has been shown to lead to immediate prophylactic treatment, including drug treatment or implantation of pacemakers or cardioverter defibrillators (9). However, cascade screening is not effective unless at-risk relatives are first notified of their risk, the health implications of the inherited condition in their family, the availability of testing, with subsequent uptake. However, uptake of genetic counseling and predictive genetic testing has been shown to be inadequate (10). While there is support from payers, public health, and health care providers (HCPs) regarding the importance of cascade testing, how best to inform relatives of their risk and systematically implement cascade testing has yet to be determined. Psychological, educational, geographical, and other barriers exist to family communication of genetic risk information. Ethical factors and family dynamics, including maintenance of confidentiality and privacy, potential for psychological harm and genetic discrimination (i.e., life insurance), balancing the right “not to know” with “duty to warn,” among others, must be considered (11). The currently recommended approach for FH, made by the International FH Foundation, includes the following: (1) the proband’s HCP should construct a pedigree that facilitates identification of at-risk relatives who should be offered testing; (2) the HCP should discuss risk notification with the proband; and (3) the proband should be provided with written information that includes general information about the family’s condition, the benefit, and availability of preventive therapies, emphasizes health consequences without testing, and be encouraged to share this with relatives (12). This approach should be taken with other highly penetrant autosomal dominant conditions. In one study specific to inherited arrhythmias and cardiomyopathies, probands were asked to distribute “family letters” containing information on risks, genetic and other screening tests, and preventive options to relatives at risk. In this study, 57% of informed relatives underwent screening (80% in arrhythmia families; 45% in cardiomyopathy families), and this was statistically significant when compared to the group where no family letter was provided (35%). While such “family letters” increased the number of relatives who presented for evaluation, over 50% in cardiomyopathy families and 43% overall of at-risk relatives had no documentation that they underwent cascade evaluation (13). It has been suggested that it is not outrageous to expect that clinicians, once they have diagnosed a patient with a genetic arrhythmia, “track down” all at-risk family members and determine their genetic status (1). However, realistically, implementation of this approach is problematic since many health care systems do not support this type of family-centric care model. Specifically, a recent review presents health policy-related limitations faced in the United States to effective implementation of cascade screening and includes (1) a low rate of reimbursement for comprehensive genetic counseling services; (2) an individual, versus family-centric, approach to prevention and insurance coverage; (3) insufficient genetic risk assessment and knowledge by a majority of HCPs without genetics credentials; and (4) a shortage of genetics specialists (in rural areas especially) (14). In order to begin addressing and overcoming these challenges, research should be conducted demonstrating effectiveness of novel methods and tools that have the capacity to efficiently notify relatives of risk. These tools should provide education, offer support, and provide attainable next steps with calls to action so that probands can be assisted, and their relatives can understand their own risk and be supported to act on it. Different methods of informing relatives of risk exist including (1) proband, or family-mediated, contact; (2) proband, or family-mediated, contact with assistance (provision of materials, such as a family letter or other written information aids, by the HCP to the proband); and (3) direct contact of at-risk relatives by the clinical service itself. Research suggests that clinical providers may take an active approach and directly contact relatives to notify them of their risk without compromising privacy or autonomy, with significantly higher numbers of relatives whose genetic status is clarified for greater efficiency, and with high levels of acceptability (15–18). A thematic analysis of FH proband interviews found that probands believed they had insufficient authority or control to persuade family members to attend screening and that they welcomed greater assistance from the clinic for contact with relatives (19). Also in support of direct contact is increased accuracy, as errors may occur in proband-mediated transmission of genetic testing result information through families (20). However, a prior study found that FH patients who expressed a preference regarding cascading method favored indirect contact because they considered it less threatening to family members (21). A genetic counseling intervention study that offered direct contact to the index patient as a last option for assistance in informing at-risk relatives reported no uptake; only eight index patients were offered this service, however, and none of the patients in this study had cardiovascular phenotypes (22). A recent literature review concluded that most studies support direct contact of relatives via letter mailed from the provider and that provider-initiated communication more often resulted in relatives being tested compared to other methods of communication (16). Regarding additional Tier 1 conditions, a prospective study of families with BRCA mutations associated with Hereditary Breast Ovarian Cancer syndrome compared proband-mediated contact to a direct contact intervention protocol that included a letter and subsequent phone call to at-risk relatives (17). This study concluded that the direct contact protocol nearly doubled the number of relatives tested and was also found to be psychologically safe. A direct contact study in families with Lynch syndrome, or hereditary non-polyposis colorectal cancer, demonstrated high approval in those who consented to participate, with a third of newly diagnosed mutation carriers having cancer identified in their first post-test colonoscopy. This type of data demonstrates acceptability of direct contact risk notification programs, as well as efficacy, feasibility, and also ethical responsibility. From the perspective of those potentially at risk, a study conducted in Australia assessing community members’ viewpoints showed that over 90% of respondents indicated their desire to be informed about a familial risk of FH and to be offered screening, with evidence of strong community support for direct contact by an FH clinic (23). The “right to know” must also be considered. Research evaluating genetic counseling interventions focused on strengthening family communication, the number of relatives informed of risk, and the impact on uptake of genetics services is ongoing and will help inform future efforts (22, 24). A randomized controlled trial studying whether a specifically designed genetic counseling intervention that included telephone support up to three times post new genetic diagnosis showed no overall significant difference for the level of family communication between the intervention and control groups (25). In this study, the level of family communication was the highest for conditions with appropriate treatments or active surveillance, such as LQTS and hypertrophic cardiomyopathy. While promising, the level still only reached ~30%. These data again beg the question regarding the potential role for direct contact, especially in “high stakes” conditions. Most, if not all, of the research conducted to date specific to direct contact has been done outside the United States. Therefore, there is a real need for research to determine whether direct contact methods would be acceptable to probands, at-risk relatives, and HCPs within the United States. How many probands might indeed welcome and appreciate this assistance and support and opt in to programs that work with and/or for them to assist in disclosure of risk information to relatives? This opinion piece does not propose that we break probands’ confidentiality and throw privacy to the wind. Instead, it hopes to promote additional conversation and brainstorming that may lead to the development and testing of innovative models of care for probands with highly penetrant, yet manageable conditions. The ultimate goal is that we will have greater impact in our work with these families where there are clear risk-reducing interventions. Probands and family members should be engaged in shaping these models and the research testing them, starting now! The next question becomes, what is feasible now in the landscape of our current health care system? Can we systematize the collection of informed consent from probands to directly share their protected health information with relatives for which they provide the clinic contact information? Can we offer probands active assistance in family communication of genetic risk information? In the pediatric setting, is there a role for standardized direct contact of HCPs caring for the at-risk children in our pedigrees with FH, other Tier One conditions, and beyond? This may be a service welcomed by the affected parent proband, who may appreciate greater assistance in coordination of care for their at-risk children and other pediatric members of their family. Advances in web-based technologies and novel models for the delivery of genetic counseling may be able to bring cascade testing more effectively and efficiently to larger numbers of at-risk relatives. For example, home-based online genetic counseling sessions for cardiovascular genetic cascade screening can be effective (26), allowing at-risk individuals to access their genetic risk information at the time of their choosing and without having to travel to a hospital or clinic, a barrier mentioned previously. In addition, interactive e-learning and decisional support e-tools available via informative websites and mobile applications have been used in pre-test genetic counseling with high knowledge and satisfaction, leading toward the “e-informed” patient (27). Mobile health applications have been shown to result in more “activated” patients – defined as individuals who believe their roles are important, that they have the confidence and knowledge needed to take action, and that they can engage in health-promoting behaviors (28), such as predictive genetic testing. Probands with higher activation may lead toward more at-risk relatives notified of their risk. In turn, e-learning information, such as an informational video about the family’s inherited cardiovascular disease, could then be delivered to relatives, who may then become activated themselves to pursue cascade testing. The power of preventive genetic and genomic information is real – that is not the question. How to ensure this information gets into the hands of all that need it, including children, however, needs more active attention. In conclusion, a powerful quote from Newson and Humphries (11): “Our biology does not stop: the risk of developing coronary heart disease as a consequence of FH will still be present, even if relatives live in ignorance.” The author confirms being the sole contributor of this work and approved it for publication. The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. 1. Schwartz PJ. Cascades or waterfalls, the cataracts of genetic screening are being opened on clinical cardiology. J Am Coll Cardiol (2010) 55(23):2577–9. doi: 10.1016/j.jacc.2009.12.064 CrossRef Full Text | Google Scholar 2. Hallowell N, Foster C, Eeles R, Ardern-Jones A, Murday V, Watson M. Balancing autonomy and responsibility: the ethics of generating and disclosing genetic information. J Med Ethics (2003) 29(2):74–9. doi:10.1136/jme.29.2.74 PubMed Abstract | CrossRef Full Text | Google Scholar 3. Centers for Disease Control and Prevention. Genomic Tests and Family Healthy History by Levels of Evidence. (2016). Available from: http://www.cdc.gov/genomics/gtesting/tier.htm Google Scholar 4. Sturm AC. The role of genetic counselors for patients with familial hypercholesterolemia. Curr Genet Med Rep (2014) 2(2):68–74. doi:10.1007/s40142-014-0036-8 CrossRef Full Text | Google Scholar 5. Nordestgaard BG, Chapman MJ, Humphries SE, Ginsberg HN, Masana L, Descamps OS, et al. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society. Eur Heart J (2013) 34(45):3478a–90a. doi:10.1093/eurheartj/eht273 PubMed Abstract | CrossRef Full Text | Google Scholar 6. Marks D, Wonderling D, Thorogood M, Lambert H, Humphries SE, Neil HA. Screening for hypercholesterolaemia versus case finding for familial hypercholesterolaemia: a systematic review and cost-effectiveness analysis. Health Technol Assess (2000) 4(29):1–123. doi:10.3310/hta4290 PubMed Abstract | CrossRef Full Text | Google Scholar 7. Marks D, Wonderling D, Thorogood M, Lambert H, Humphries SE, Neil HA. Cost effectiveness analysis of different approaches of screening for familial hypercholesterolaemia. BMJ (2002) 324(7349):1303. doi:10.1136/bmj.324.7349.1303 PubMed Abstract | CrossRef Full Text | Google Scholar 8. Nherera L, Marks D, Minhas R, Thorogood M, Humphries SE. Probabilistic cost-effectiveness analysis of cascade screening for familial hypercholesterolaemia using alternative diagnostic and identification strategies. Heart (2011) 97(14):1175–81. doi:10.1136/hrt.2010.213975 PubMed Abstract | CrossRef Full Text | Google Scholar 9. Hofman N, Tan HL, Alders M, van Langen IM, Wilde AA. Active cascade screening in primary inherited arrhythmia syndromes: does it lead to prophylactic treatment? J Am Coll Cardiol (2010) 55(23):2570–6. doi:10.1016/j.jacc.2009.12.063 PubMed Abstract | CrossRef Full Text | Google Scholar 10. Christiaans I, Birnie E, Bonsel GJ, Wilde AA, van Langen IM. Uptake of genetic counselling and predictive DNA testing in hypertrophic cardiomyopathy. Eur J Hum Genet (2008) 16(10):1201–7. doi:10.1038/ejhg.2008.92 PubMed Abstract | CrossRef Full Text | Google Scholar 11. Newson AJ, Humphries SE. Cascade testing in familial hypercholesterolaemia: how should family members be contacted? Eur J Hum Genet (2005) 13(4):401–8. doi:10.1038/sj.ejhg.5201360 CrossRef Full Text | Google Scholar 12. Watts GF, Gidding S, Wierzbicki AS, Toth PP, Alonso R, Brown WV, et al. Integrated guidance on the care of familial hypercholesterolaemia from the International FH Foundation. Int J Cardiol (2014) 171(3):309–25. doi:10.1016/j.ijcard.2013.11.025 PubMed Abstract | CrossRef Full Text | Google Scholar 13. van der Roest WP, Pennings JM, Bakker M, van den Berg MP, van Tintelen JP. Family letters are an effective way to inform relatives about inherited cardiac disease. Am J Med Genet A (2009) 149A(3):357–63. doi:10.1002/ajmg.a.32672 PubMed Abstract | CrossRef Full Text | Google Scholar 14. George R, Kovak K, Cox SL. Aligning policy to promote cascade genetic screening for prevention and early diagnosis of heritable diseases. J Genet Couns (2015) 24(3):388–99. doi:10.1007/s10897-014-9805-5 PubMed Abstract | CrossRef Full Text | Google Scholar 15. Suthers GK, Armstrong J, McCormack J, Trott D. Letting the family know: balancing ethics and effectiveness when notifying relatives about genetic testing for a familial disorder. J Med Genet (2006) 43(8):665–70. doi:10.1136/jmg.2005.039172 PubMed Abstract | CrossRef Full Text | Google Scholar 16. Allison M. Communicating risk with relatives in a familial hypercholesterolemia cascade screening program: a summary of the evidence. J Cardiovasc Nurs (2015) 30(4):E1–12. doi:10.1097/JCN.0000000000000153 PubMed Abstract | CrossRef Full Text | Google Scholar 17. Sermijn E, Delesie L, Deschepper E, Pauwels I, Bonduelle M, Teugels E, et al. The impact of an interventional counselling procedure in families with a BRCA1/2 gene mutation: efficacy and safety. Fam Cancer (2016) 15(2):155–62. doi:10.1007/s10689-015-9854-4 PubMed Abstract | CrossRef Full Text | Google Scholar 18. van Maarle MC, Stouthard MEA, Marang-van de Mheen PJ, Klazinga NS, Bonsel GJ. How disturbing is it to be approached for a genetic cascade screening programme for familial hypercholesterolaemia? Psychological impact and screenees’ views. Community Genet (2001) 4(4):244–52. doi:10.1159/000064200 CrossRef Full Text | Google Scholar 19. Hardcastle SJ, Legge E, Laundy CS, Egan SJ, French R, Watts GF, et al. Patients’ perceptions and experiences of familial hypercholesterolemia, cascade genetic screening and treatment. Int J Behav Med (2015) 22(1):92–100. doi:10.1007/s12529-014-9402-x PubMed Abstract | CrossRef Full Text | Google Scholar 20. Vos J, Menko F, Jansen AM, van Asperen CJ, Stiggelbout AM, Tibben A. A whisper-game perspective on the family communication of DNA-test results: a retrospective study on the communication process of BRCA1/2-test results between proband and relatives. Fam Cancer (2011) 10(1):87–96. doi:10.1007/s10689-010-9385-y PubMed Abstract | CrossRef Full Text | Google Scholar 21. Hallowell N, Jenkins N, Douglas M, Walker S, Finnie R, Porteous M, et al. Patients’ experiences and views of cascade screening for familial hypercholesterolemia (FH): a qualitative study. J Community Genet (2011) 2(4):249–57. doi:10.1007/s12687-011-0064-y PubMed Abstract | CrossRef Full Text | Google Scholar 22. Forrest LE, Burke J, Bacic S, Amor DJ. Increased genetic counseling support improves communication of genetic information in families. Genet Med (2008) 10(3):167–72. doi:10.1097/GIM.0b013e318164540b PubMed Abstract | CrossRef Full Text | Google Scholar 23. Maxwell SJ, Molster CM, Poke SJ, O’Leary P. Communicating familial hypercholesterolemia genetic information within families. Genet Test Mol Biomarkers (2009) 13(3):301–6. doi:10.1089/gtmb.2008.0138 PubMed Abstract | CrossRef Full Text | Google Scholar 24. Gaff C, Hodgson J. A genetic counseling intervention to facilitate family communication about inherited conditions. J Genet Couns (2014) 23(5):814–23. doi:10.1007/s10897-014-9696-5 PubMed Abstract | CrossRef Full Text | Google Scholar 25. Hodgson J, Metcalfe S, Gaff C, Donath S, Delatycki MB, Winship I, et al. Outcomes of a randomised controlled trial of a complex genetic counselling intervention to improve family communication. Eur J Hum Genet (2016) 24(3):356–60. doi:10.1038/ejhg.2015.122 PubMed Abstract | CrossRef Full Text | Google Scholar 26. Otten E, Birnie E, Ranchor AV, van Langen IM. Online genetic counseling from the providers’ perspective: counselors’ evaluations and a time and cost analysis. Eur J Hum Genet (2016). doi:10.1038/ejhg.2015.283 CrossRef Full Text | Google Scholar 27. Birch PH. Interactive e-counselling for genetics pre-test decisions: where are we now? Clin Genet (2015) 87(3):209–17. doi:10.1111/cge.12430 PubMed Abstract | CrossRef Full Text | Google Scholar 28. Ledford CJ, Ledford CJ, Canzona MR, Cafferty LA, Hodge JA. Mobile application as a prenatal education and engagement tool: a randomized controlled pilot. Patient Educ Couns (2015) 99(4):578–82. doi:10.1016/j.pec.2015.11.006 CrossRef Full Text | Google Scholar Keywords: direct contact, cardiovascular genetics, familial hypercholesterolemia, genetic counselor, genetic counseling, cascade screening, cascade testing, genetic testing Citation: Sturm AC (2016) Cardiovascular Cascade Genetic Testing: Exploring the Role of Direct Contact and Technology. Front. Cardiovasc. Med. 3:11. doi: 10.3389/fcvm.2016.00011 Received: 22 February 2016; Accepted: 05 April 2016; Published: 19 April 2016 Edited by: Reviewed by: Copyright: © 2016 Sturm. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: Amy C. Sturm, [email protected]
Journal of Communications and Networks, Volume 18, pp 1-1; https://doi.org/10.1109/jcn.2016.000016

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JCN invites submission of high-quality papers in the areas of Communication Theory and Systems, Wireless Communications, and Networks and Services. JCN is an all-electronic processing journal that stresses fast turnaround together with expert peer reviewing. Typically, a manuscript, if accepted after one round of review, will be ready for publication in about 6 months after submission, with 2~3 months additionally required for each additional round of review. SUBMISSION A prospective author should submit the manuscript for publication consideration in electronic file (LaTex) via JCN website (http://jcn.or.kr) to an appropriate Division Editor (for regular papers) or to any Guest Editor of an appropriate Special Issue. (Special Issues may include regular papers to avoid unnecessary publication delays.)
Journal of Communications and Networks, Volume 17, pp 1-1; https://doi.org/10.1109/jcn.2015.000093

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JCN invites submission of high-quality papers in the areas of Communication Theory and Systems, Wireless Communications, and Networks and Services. JCN is an all-electronic processing journal that stresses fast turnaround together with expert peer reviewing. Typically, a manuscript, if accepted after one round of review, will be ready for publication in about 6 months after submission, with 2~3 months additionally required for each additional round of review. SUBMISSION A prospective author should submit the manuscript for publication consideration in electronic file (LaTex) via JCN website (http://jcn.or.kr) to an appropriate Division Editor (for regular papers) or to any Guest Editor of an appropriate Special Issue. (Special Issues may include regular papers to avoid unnecessary publication delays.)
Journal of Communications and Networks, Volume 16, pp 1-1; https://doi.org/10.1109/jcn.2014.000116

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JCN invites submission of high-quality papers in the areas of Communication Theory and Systems, Wireless Communications, and Networks and Services. JCN is an all-electronic processing journal that stresses fast turnaround together with expert peer reviewing. Typically, a manuscript, if accepted after one round of review, will be ready for publication in about 6 months after submission, with 2~3 months additionally required for each additional round of review. SUBMISSION A prospective author should submit the manuscript for publication consideration in electronic file (LaTex or MS Word) via JCN website (http://jcn.or.kr) to an appropriate Division Editor (for regular papers) or to any Guest Editor of an appropriate Special Issue. (Special Issues may include regular papers to avoid unnecessary publication delays.) JCN is available in both paper and electronic formats, with the latter available in the JCN Digital Library in the JCN Web site, http://jcn.or.kr. For further details on paper submission and subscription, please visit the JCN Web site.
Frontiers in Systems Neuroscience, Volume 8; https://doi.org/10.3389/fnsys.2014.00187

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The motivations behind the development of many neuromorphic processors have been dominated by either the creation of better artificial intelligence, or novel non-von Neumann computing paradigms. A result of this impetus has been a number of low-power processors capable of simulating many different biological features of the nervous system. Power efficiency is crucial for deployed neuromorphic systems, but it also opens this technology up to other energy restricted applications. In this opinion, we suggest two such applications pertaining to therapeutic stimulation of the nervous system where closing the control loop could be assisted by advances in neuromorphic architectures: (1) deep brain stimulation (DBS) in the treatment of Parkinson's disease and (2) epidural spinal cord stimulation (ESS) for restoring voluntary motor functions. Though there are still questions that must be addressed before this would be feasible, but we are suggesting that the technological barriers—in both the algorithms and hardware—can be overcome with directed funding and research. Neuromorphic processor research is centered around the creation of brain-like intelligence through power-efficient circuits that borrow elements directly from biology (Mead, 1989). The applications for these projects range from brain-scale simulations (Gao et al., 2012; Benjamin et al., 2014) and in silica experimentation (Schemmel et al., 2010; Furber et al., 2012), to brain-like computing and learning (Merolla et al., 2011; Srinivasa and Cruz-Albrecht, 2012; Cruz-Albrecht et al., 2013; Rahimi Azghadi et al., 2014; Schmuker et al., 2014). These projects promise unrivaled access to large-scale models of the brain as well as insight into the unique non-von Neumann computation that biological systems appear to achieve. Regardless of the motivation, the tangible result of these efforts has been an accumulation of low-power circuits capable of emulating various elements of the nervous system. Although these are essential for embodying robotic systems and augmenting current super-computing paradigms, they also have the potential to assist in nervous system stimulation control. This application is outside the scope of the currently funded neuromorphic hardware projects, but with new insights and technological advances, it is one that will be particularly beneficial. In our current capacity to monitor neural circuits, most system variables are unobservable. One strategy for estimating these unknown system variables and parameters is by employing an Unscented Kalman Filter (UKF) to combine the observable and unobservable states. The UKF employs a set of known dynamical equations and observation functions with the measurable data to update an approximation of the state and its uncertainty. At each update, sigma points—system states that are consistent with the current state uncertainty—are selected and used to integrate the system. These are combined with estimated mean state values and the approximate uncertainty. A gain matrix then updates the new most likely state of the system. The schematic for this organization is illustrated in Figure 1A. Applying this kind of feedback control to biological systems was initially demonstrated by Voss et al. (2004) but has since been demonstrated on a number of control and estimation problems (Abarbanel et al., 2008; Li et al., 2009; Ullah and Schiff, 2009, 2010; Schiff, 2010; ODoherty et al., 2011; Aprasoff and Donchin, 2012; Schiff, 2012; Liu et al., 2014). Figure 1. Example therapeutic applications of model based control. (A) The system dynamics are described by a model, F, and the observations are described by a function, A. In most systems those observations are going to be noisy, so a covariance matrix, R, will account for that. After one step of F, using the resulting sigma points will provide X˜i = F(Xi). A new set of observations can then be found, i = A(Xi). The means over these two matrices are the a priori state and measurement estimates. The a posteriori state estimate, x^, is now dependent on the state estimate, x˜, the measurement estimate, ỹ, the actual measurement, y, and the Kalman gain matrix, G. (B) Diagram of deep brain stimulation in the treatment of Parkinson's disease. Adapted from Thibeault and Srinivasa (2013). (C) Example epidural spinal cord stimulation for restoring voluntary motor functions. By using a model of the area under stimulation, both the activity and state of that area can be approximated—something that is not directly measurable. The model, constructed from the current understanding of the anatomy, can then be used to find an optimal set of stimulation parameters. In addition, the model output can be used as the feedback into a control system that can not only dynamically tune the stimulation parameters but also adapt to the physiological circuit remodeling—providing the highest possible therapeutic benefit. Embedding these models in low-power neuromorphic hardware would facilitate a transition into implantable devices. A discussion of control inherently implies observability of the system. However, observability alone is useful to current nervous system stimulation strategies. Observing the unknown—or unreachable—states of the physical system, would provide a way to automatically tune the stimulation parameters—assisting clinicians in finding the optimal set points in open-loop control. Finally, in addition to the UKF there are other model-based control schemes that could be employed here. The application of DBS to patients with pharmacoresistant Parkinson's disease can be traced back to the early 1980's (Montgomery Jr, 2012). In DBS, dual electrodes are implanted bilaterally into the nuclei of the basal ganglia (see Figure 1B)—the current target is the subthalamic nucleus. Constant electrical pulses are then injected into the electrodes. After implantation, clinicians will experiment with frequency, amplitude, and duration of those electrical pulses to find a configuration with the highest benefit. Finding that point however, is an inexact science and periodic adjustments to compensate for neural plasticity are required. Although there is a proven clinical benefit to DBS, there is no clear explanation for its mechanism of action. Although the open-loop configuration of DBS has proven capable, closed-loop control of DBS has been shown to be a more effective treatment in both theoretical (Santaniello et al., 2011), and physiological experiments (Rosin et al., 2011). For example, in Rosin et al. (2011) a simple feedback loop was created where the activation of the DBS pulse was triggered by spiking in a reference structure—either the internal segment of the globus pallidus or primary motor cortex. The control paradigm demonstrated a larger reduction in pallidal oscillations and akinesia compared to open-loop DBS. The resulting system—although brilliantly designed—is an incredibly simple solution and one that exemplifies the therapeutic advances that can be made with adaptive feedback control systems. The class of model-based control of DBS suggested here has already been demonstrated in simulation space by Schiff (2010) using the simple neuron implementation of Rubin and Terman (2004). Although the mathematical model used in that study was computationally cheaper than the alternative, it is still difficult to simulate in a low-power microprocessor. Aspects of the original Rubin and Terman (2004) results were implemented using a more hardware friendly model in Thibeault and Srinivasa (2013), however, the required level of abstraction in a control paradigm is still unclear. Despite unanswered questions, these studies are encouraging and demonstrate the feasibility of the strategy. The recent discoveries in the use of epidural spinal cord stimulation—diagrammed in Figure 1C—on patients with motor complete paraplegia has revealed a therapeutic pathway toward restoring voluntary motor function (Harkema et al., 2011). However, the mechanisms behind this benefit as well as the supporting technology is still immature. The current state-of-the-art involves randomly tuning the stimulation parameters manually until a physiological improvement is observed—these parameters include both the duration and amplitude of the stimulus as well as anode/cathode pairings. There have been efforts to apply Bayesian optimization approaches to automating the parameter search but these did not directly account for the relevant biological structures (Desautels, 2014). Additionally, it has been suggested that the therapeutic restoration of motor control is mechanistically dependent on the remodeling of the remaining spinal circuits (van den Brand et al., 2012). Having a control strategy as well as a model that are adaptive to the plastic changes within the spinal circuits would require less manual parameter adjustments over the life of the implant. As a clinical treatment, ESS is still underdeveloped. However, it is one that could benefit from a model-based control strategy—either as an observer system for parametric optimization or as a complete closed-loop solution. Although the fidelity of the spinal cord model and the source of sensory feedback have not been fully explored, in many ways this appears to be a more tractable problem compared to DBS—it may also prove to be an ideal alternative as well (Fuentes et al., 2009). The accessibility of the spinal cord as well as the simplicity of the microcircuit may make closing the loop on ESS more feasible. However, if more finely tuned control of the individual muscles is required, the complexity of the problem could quickly out pace that of DBS. Despite the technological and theoretical advances outlined here, there are still obstacles to overcome. Where and what to measure when closing the loop for both DBS and ESS is not entirely clear. The stability of the hardware measuring those signals is also a concern. Furthermore, as mentioned above, the appropriate level of biological fidelity required in the model has not been fully resolved. The proposed use of neuromorphic hardware implies that the model for the control system actually requires high-fidelity. In DBS treatment of Parkinson's disease this appears to be the case. However, for spinal-cord stimulation, it may not be required. Regardless, closed-loop strategies are clearly more effective and the theoretical and technological barriers are low enough that a concerted effort should be made to advance this concept toward clinical treatments. Finally, model-based control strategies will not only improve the therapeutic benefit but the power consumption as well. Rather than blindly applying stimulation, pulses can be applied only as needed. Utilizing neuromorphic hardware will add to that power savings by both reducing the computational burden and providing the necessary biological detail for model-based control. The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Abarbanel, H. D. I., Creveling, D. R., and Jeanne, J. M. (2008). Estimation of parameters in nonlinear systems using balanced synchronization. Phys. Rev. E 77:016208. doi: 10.1103/PhysRevE.77.016208 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Aprasoff, J., and Donchin, O. (2012). Correlations in state space can cause sub-optimal adaptation of optimal feedback control models. J. Comput. Neurosci. 32, 297–307. doi: 10.1007/s10827-011-0350-z Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Benjamin, B. V., Gao, P., McQuinn, E., Choudhary, S., Chandrasekaran, A. R., Bussat, J., et al. (2014). Neurogrid: a mixed-analog-digital multichip system for large-scale neural simulations. Proc. IEEE 102, 699–716. doi: 10.1109/JPROC.2014.2313565 CrossRef Full Text | Google Scholar Cruz-Albrecht, J. M., Derosier, T., and Srinivasa, N. (2013). A scalable neural chip with synaptic electronics using cmos integrated memristors. Nanotechnology 24:384011. doi: 10.1088/0957-4484/24/38/384011 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Desautels, T. A. (2014). Spinal Cord Injury Therapy Through Active Learning. Ph.D. thesis, California Institute of Technology, Pasadena, CA. Google Scholar Fuentes, R., Petersson, P., Siesser, W. B., Caron, M. G., and Nicolelis, M. A. (2009). Spinal cord stimulation restores locomotion in animal models of parkinson's disease. Science 323, 1578–1582. doi: 10.1126/science.1164901 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Furber, S., Lester, D., Plana, L., Garside, J., Painkras, E., Temple, S., et al. (2012). Overview of the spinnaker system architecture. Comput. IEEE Trans. 62, 2454–2467. doi: 10.1109/TC.2012.142 CrossRef Full Text | Google Scholar Gao, P., Benjamin, B. V., and Boahen, K. (2012). Dynamical system guided mapping of quantitative neuronal models onto neuromorphic hardware. Circ. Syst. I Regul. Papers IEEE Trans. 59, 2383–2394. doi: 10.1109/TCSI.2012.2188956 CrossRef Full Text | Google Scholar Harkema, S., Gerasimenko, Y., Hodes, J., Burdick, J., Angeli, C., Chen, Y., et al. (2011). Effect of epidural stimulation of the lumbosacral spinal cord on voluntary movement, standing, and assisted stepping after motor complete paraplegia: a case study. Lancet 377, 1938–1947. doi: 10.1016/S0140-6736(11)60547-3 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Li, Z., O'Doherty, J. E., Hanson, T. L., Lebedev, M. A., Henriquez, C. S., and Nicolelis, M. A. (2009). Unscented kalman filter for brain-machine interfaces. PLoS ONE 4:e6243. doi: 10.1371/journal.pone.0006243 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Liu, C., Wang, J., Li, H., Xue, Z., Deng, B., and Wei, X. (2014). Model-based iterative learning control of parkinsonian state in thalamic relay neuron. Commun. Nonlin. Sci. Numer. Simul. 19, 3255–3266. doi: 10.1016/j.cnsns.2014.02.008 CrossRef Full Text | Google Scholar Mead, C. (1989). Analog VLSI and Neural Systems. Reading: Addison-Wesley. doi: 10.1007/978-1-4613-1639-8 Merolla, P., Arthur, J., Akopyan, F., Imam, N., Manohar, R., and Modha, D. (2011). “A digital neurosynaptic core using embedded crossbar memory with 45pj per spike in 45nm,” in Custom Integrated Circuits Conference (CICC), 2011 IEEE (San Jose, CA), 1–4. Google Scholar Montgomery, E. B. Jr. (2012). The epistemology of deep brain stimulation and neuronal pathophysiology. Front. Integr. Neurosci. 6:78. doi: 10.3389/fnint.2012.00078 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar ODoherty, J. E., Lebedev, M. A., Ifft, P. J., Zhuang, K. Z., Shokur, S., Bleuler, H., et al. (2011). Active tactile exploration using a brain-machine-brain interface. Nature 479, 228–231. doi: 10.1038/nature10489 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Rahimi Azghadi, M., Iannella, N., Al-Sarawi, S. F., Indiveri, G., and Abbott, D. (2014). Spike-based synaptic plasticity in silicon: design, implementation, application, and challenges. Proc. IEEE 102, 717–737. doi: 10.1109/JPROC.2014.2314454 CrossRef Full Text | Google Scholar Rosin, B., Slovik, M., Mitelman, R., Rivlin-Etzion, M., Haber, S., Israel, Z., et al. (2011). Closed-loop deep brain stimulation is superior in ameliorating parkinsonism. Neuron 72, 370–384. doi: 10.1016/j.neuron.2011.08.023 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Rubin, J., and Terman, D. (2004). High frequency stimulation of the subthalamic nucleus eliminates pathological thalamic rhythmicity in a computational model. J. Comput. Neurosci. 16, 211–235. doi: 10.1023/B:JCNS.0000025686.47117.67 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Santaniello, S., Fiengo, G., Glielmo, L., and Grill, W. M. (2011). Closed-loop control of deep brain stimulation: a simulation study. Neural Syst. Rehabil. Eng. IEEE Trans. 19, 15–24. doi: 10.1109/TNSRE.2010.2081377 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Schemmel, J., Brüderle, D., Grübl, A., Hock, M., Meier, K., and Millner, S. (2010). “A wafer-scale neuromorphic hardware system for large-scale neural modeling,” in Proceedings of the 2010 IEEE International Symposium on Circuits and Systems (ISCAS"10) (Paris), 1947–1950. Google Scholar Schiff, S. J. (2010). Towards model-based control of parkinson's disease. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 368, 2269–2308. doi: 10.1098/rsta.2010.0050 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Schiff, S. J. (2012). Neural Control Engineering The Emerging Intersection between Control Theory and Neuroscience. Cambridge, MA: The MIT Press. Google Scholar Schmuker, M., Pfeil, T., and Nawrot, M. P. (2014). A neuromorphic network for generic multivariate data classification. Proc. Natl. Acad. Sci. U.S.A. 111, 2081–2086. doi: 10.1073/pnas.1303053111 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Srinivasa, N., and Cruz-Albrecht, J. (2012). Neuromorphic adaptive plastic scalable electronics: analog learning systems. Pulse IEEE 3, 51–56. doi: 10.1109/MPUL.2011.2175639 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Thibeault, C. M., and Srinivasa, N. (2013). Using a hybrid neuron in physiologically inspired models of the basal ganglia. Front. Comput. Neurosci. 7:88. doi: 10.3389/fncom.2013.00088 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Ullah, G., and Schiff, S. J. (2009). Tracking and control of neuronal hodgkin-huxley dynamics. Phys. Rev. E 79:040901. doi: 10.1103/PhysRevE.79.040901 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Ullah, G., and Schiff, S. J. (2010). Assimilating seizure dynamics. PLoS Comput. Biol. 6:e1000776. doi: 10.1371/journal.pcbi.1000776 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar van den Brand, R., Heutschi, J., Barraud, Q., DiGiovanna, J., Bartholdi, K., Huerlimann, M., et al. (2012). Restoring voluntary control of locomotion after paralyzing spinal cord injury. Science 336, 1182–1185. doi: 10.1126/science.1217416 Pubmed Abstract | Pubmed Full Text | CrossRef Full Text | Google Scholar Voss, H. U., Timmer, J., and Kurths, J. (2004). Nonlinear dynamical system identification from uncertain and indirect measurements. Int. J. Bifurcation Chaos 14, 1905–1933. doi: 10.1142/S0218127404010345 CrossRef Full Text | Google Scholar Keywords: model-based control, neuromorphic hardware, deep brain stimulation, brain augmentation, spinal-cord stimulation Citation: Thibeault CM (2014) A role for neuromorphic processors in therapeutic nervous system stimulation. Front. Syst. Neurosci. 8:187. doi: 10.3389/fnsys.2014.00187 Received: 12 August 2014; Accepted: 16 September 2014; Published online: 07 October 2014. Edited by: Reviewed by: Copyright © 2014 HRL Laboratories LLC. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. *Correspondence: [email protected]
Journal of Communications and Networks, Volume 16, pp 1-1; https://doi.org/10.1109/jcn.2014.000081

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Journal of Communications and Networks, Volume 15, pp 1-1; https://doi.org/10.1109/jcn.2013.000058

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Journal of Communications and Networks, Volume 15, pp 1-1; https://doi.org/10.1109/jcn.2013.000056

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The Journal of Communications and Networks (JCN) invites submission of high-quality papers in the areas of Communication Theory and Systems, Wireless Communications, and Networks and Services. JCN is an all-electronic processing journal that stresses fast turnaround together with expert peer reviewing. Typically, a manuscript, if accepted after one round of review, will be ready for publication in about 6 months after submission, with 2~3 months additionally required for each additional round of review. SUBMISSION A prospective author should submit the manuscript for publication consideration in electronic file (LaTex or MS Word) via JCN website (http://jcn.or.kr) to an appropriate Division Editor (for regular papers) or to any Guest Editor of an appropriate Special Issue. (Special Issues may include regular papers to avoid unnecessary publication delays.) JCN is available in both paper and electronic formats, with the latter available in the JCN Digital Library in the JCN Web site, http://jcn.or.kr. For further details on paper submission and subscription, please visit the JCN Web site.
Journal of Communications and Networks, Volume 15, pp 1-1; https://doi.org/10.1109/jcn.2013.000059

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Journal of Biomolecular Structure and Dynamics, Volume 31, pp 788-808; https://doi.org/10.1080/07391102.2012.712458

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Inosine monophosphate dehydrogenase (IMPDH) is involved in de novo biosynthesis pathway of guanosine nucleotide. Type II isoform of this enzyme is selectively upregulated in lymphocytes and chronic myelogenous leukemia (CML) cells, and is an excellent target for antileukemic agent. The molecular dynamics simulation results (15 ns) of three unliganded 1B3O, 1JCN, and 1JR1 structures have clearly revealed that IN, IC (N- and C-terminal of catalytic domains) and C1, C2 (cystathionine-beta-synthase-1 and 2) domains of IMPDH enzyme have been stabilized by six conserved water (center) mediated salt bridge interactions. These conserved water molecules could be involved in interdomain or intradomain recognition, intradomain coupling, and charge transfer processes. The binding propensity of cystathionine-beta-synthase domain to catalytic domain (through conserved water-mediated salt bridges) has provided a new insight to the biochemistry of IMPDH. Stereospecific interaction of IN with C2 domain through conserved water molecule (K109–WII1–D215/D216) is observed to be unique in the simulated structure of hIMPDH-II. The geometrical/structural consequences and topological feature around the WII1 water center may be utilized for isoform specific inhibitor design for CML cancer. An animated Interactive 3D Complement (I3DC) is available in Proteopedia at http://proteopedia.org/w/Journal:JBSD:1
Journal of Communications and Networks, Volume 15; https://doi.org/10.1109/jcn.2013.000002

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Provides a listing of current committee members and society officers.
Journal of Communications and Networks, Volume 15; https://doi.org/10.1109/jcn.2013.000020

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Aim and scope: The Journal of Communications and Networks is published four times per year, and is committed to publishing high-quality papers that advance the state-of-the-art and practical applications of communications and information networks. Theoretical research contributions presenting new techniques, concepts, or analyses, applied contributions reporting on experiences and experiments, and tutorial expositions of permanent reference value are welcome. The subjects covered by this journal include all topics in communication theory and systems, wireless communications, and networks and services.
Journal of Communications and Networks, Volume 15, pp 1-1; https://doi.org/10.1109/jcn.2013.000018

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