Journal of Human Physiology

Journal Information
EISSN : 2661-3859
Current Publisher: Bilingual Publishing Co. (10.30564)
Total articles ≅ 6

Articles in this journal

Agussalim A, M. Natsir, La Jumu, Sukatemin Sukatemin, Sisilia Teresia Rosmala Dewi
Journal of Human Physiology, Volume 2; doi:10.30564/jhp.v2i2.2584

IntroductionOnce the enormity of online games took over the attention of many teens and children so that it brought a big change. Aggressive behavior among adolescents especially high school students from year to year is increasing both in number and forms of aggressive behavior that is raised.ObjectivesThis study aims to determine the relationship between playing online games and aggressive behavior of high school students in Jayapura.MethodsThe research method used was analytical research using cross sectional design. Study the relationship between two variables in a situation or group of objects using a simple linear regression statistical test.ResultThe correlation effect of Length Playing Game Online with aggressively behavior of students in High School logistic test results obtained meaningful results where the value of p = 0, 00
Zhen Zhan, Huanhuan Cheng, Xianhong Lin, Yangyang Meng, Liying Dai, Hong Zheng, Qilian Xie
Journal of Human Physiology, Volume 2; doi:10.30564/jhp.v2i2.2666

Objective To investigate the effect of simple head cooling combined with ganglioside therapy on neonatal hypoxic-ischemic encephalopathy (HIE) and its clinical efficacy. Methods A total of 100 children with HIE admitted in the neonatal ward of our hospital from August 2018 to October 2020 were selected as the research objects, and were divided into control group and observation group according to the random number table method, with 50 cases in each group. The control group was treated with gangliosides, and the observation group was treated with simple head cooling combined with gangliosides. Observe and compare the clinical performance improvement time, the level of relevant hematological examination indexes before and after treatment, and the neonatal behavioral neurological assessment (NBNA), clinical efficacy, and adverse reactions. Results The improvement time of convulsions, disturbance of consciousness, pupil changes, hypotonia, and gastrointestinal dysfunction in the observation group was significantly lower than that in the control group (all P<0.001). After treatment, the NSE, IL-6, CK, CK-MB of the two groups of children were significantly lower than before treatment, and the serum calcium and NBNA scores were significantly higher than before treatment, and the decrease or increase in the observation group was significantly higher than that of the control Group (all P<0.001). The total effective rate of treatment of children in the observation group (82.00%) was higher than that of the control group (62.00%) (P
Surayya Ado Bala, Shri Ojha Kant, Adamu Garba Yakasai
Journal of Human Physiology, Volume 2; doi:10.30564/jhp.v2i2.2683

Over the last decade, deep learning (DL) methods have been extremely successful and widely used in almost every domain. Researchers are now focusing on the convergence of medical imaging and drug design using deep learning to revolutionize medical diagnostic and improvement in the monitoring from response to therapy. DL a new machine learning paradigm that focuses on learning with deep hierarchical models of data. Medical imaging has transformed healthcare science, it was thought of as a diagnostic tool for disease, but now it is also used in drug design. Advances in medical imaging technology have enabled scientists to detect events at the cellular level. The role of medical imaging in drug design includes identification of likely responders, detection, diagnosis, evaluation, therapy monitoring, and follow-up. A qualitative medical image is transformed into a quantitative biomarker or surrogate endpoint useful in drug design decision-making. For this, a parameter needs to be identified that characterizes the disease baseline and its subsequent response to treatment. The result is a quantifiable improvement in healthcare quality in most therapeutic areas, resulting in improvements in quality and duration of life. This paper provides an overview of recent studies on applying the deep learning method in medical imaging and drug design. We briefly discuss the fields related to the history of deep learning, medical imaging, and drug design.
Yuri Pmvovarenko
Journal of Human Physiology, Volume 2; doi:10.30564/jhp.v2i1.1608

Physiotherapists usually ignore the electric polarization of human bodies that occurs under the influence of the electromagnetic forces of the Earth. This is irrational, since the positive or negative electrification of human tissues has the opposite effect on both their properties and functional activity. How physiotherapists must take into account the polarizing effect of the electromagnetic forces of the Earth when analysing the functional states of the tissues of the human body is shown here. It also shows how these electromagnetic forces can be used by manual and physiotherapists.
Xiaohuan Chen, Lei Liu, Lei Liao, Yahui Wang, Jiacheng Shi, Hanyou Mo
Journal of Human Physiology, Volume 2; doi:10.30564/jhp.v2i1.1594

Background: The study of regulatory B cells (Bregs) in systemic lupus erythematosus (SLE) has been in full swing in recent years, but the number and function of Bregs in SLE patients have also present quite contradictory results. Therefore, we conducted a meta-analysis to verify the changes in Bregs in active SLE. Methods: We identified studies reporting the proportions of Bregs in SLE patients by searching Pubmed, Embase, Web of Science, Cochrane and CNKI. Due to the degree of heterogeneity is very high, we used a random effects model to assess the mean differences in percentages of Bregs between active SLE and controls. Then, sensitivity analysis and subgroup analysis were performed to verify potential sources of heterogeneity. Results: Seven eligible articles involving 301 active SLE patients and 218 controls were included in the meta-analysis. The pooled percentages of Bregs were found no significant difference between active SLE patients and healthy controls [0.259, (−1.150, 1.668), p = 0.719], with great heterogeneity ( I2 = 97.5%) . The result of sensitivity analysis showed that exclusion of any single study or single article did not materially resolve the heterogeneity, but after excluding the article conducted by Cai X and his colleagues, the percentages of Bregs were significantly higher in active SLE than those in controls [1.394, (0.114,2.675), p = 0.033]. The results of subgroup analysis revealed that when the disease activity was judged by SLEDAI score ≥ 5, the percentages of Bregs were significantly lower in the SLE groups than in the control groups[-1.99,(-3.241,-0.739), p = 0.002], but when the threshold of SLEDAI score ≥ 6 chosen for active SLE, the percentages of Bregs were significantly increased in the SLE groups[2.546,(1.333,3.759), p < 0.001]. Meanwhile, other subgroup analysis based on the different phenotypes of Bregs, diagnostic criteria, enrolled research countries, treatment status, and organ involvement did not differ in proportion of Bregs between SLE patients and controls. Conclusions: The study implies that Bregs may play a role in the pathogenesis of active SLE, and the thresholds of SLEDAI score to distinguish between active and inactive SLE patients are important factors affecting the percentages of Bregs.
Rajeev Gupta
Journal of Human Physiology, Volume 2; doi:10.30564/jhp.v2i1.1755

Kapalbhati is well known for improving cardiovascular health. But there are some reports of heart attacks while practising kapalbhati. We hypothesize that ill-effect of kapalbhati could be because of autonomic dysfunction to heart. In the present study, we aim to understand the acute effect of kapalbhati yoga on heart rate dynamics using heart rate variability (HRV) analysis. Resting heart rate (HR) varies widely in different individuals and during various physiological stresses, particularly, exercise it can go up to three-fold. These changes in heart rate are known as heart rate variability (HRV). Variability in heart rate reflects the control of autonomic system on the heart and which can be determined during brief periods of electrocardiographic (ECG) monitoring. HRV measures the effect of any physical exercise on the heart rate using time- and frequency-domain methods. Frequency-domain method involves power spectral analyses of the beat-to-beat intervals (R-R intervals) variability data. When total power vs. frequency, fast fourier transform analysis of R-R intervals data is done, it shows three well-defined peaks/rhythms in every individual, which contain different physiological information. Thus, the total spectral power of R-R intervals data can be divided into three components or bands viz., the very low frequency (VLF) band, the low-frequency (LF) band and the high frequency (HF) band. VLF represent very long time-period physiological phenomenon like thermoregulation, circadian rhythms etc. and thus are not seen in short-term recordings like in this work. LF band power represents long period physiological rhythms in the frequency range of 0.05- 0.15 Hz and LF band power increases as a consequence of sympathetic activation. HF band represent physiological rhythms in the frequency range of 0.15-0.5 Hz and they are synchronous with the respiration rate, and arise due to the intrathoracic pressure changes and mechanical vibrations caused by the breathing activity. In this work, twenty healthy male volunteers were trained in kapalbhati yoga and their ECG waveforms (2 min.) were obtained while doing kapalbhati (breathing at 1 Hz frequency for 2 min.) and were compared with the baseline (just 2 min. before the start) and post-kapalbhati (immediately 2 min. after completing the practice) HRV data. Our results showed a significant decrease in the time-domain measures i.e., NN50, pNN50 and the mean heart rate interval during-kapalbhati when compared statistically to the respective before practice or “pre”-kapalbhati (p < 0.05, student’s paired t-test) values. Frequency-domain indices showed that during-kapalbhati there is a significant increase (~48%) in the LF band power which suggests sympathetic activation and a significant increase (~88%) in the low frequency to the high frequency power ratio (LF/HF ratio) which indicates sympathetic system predominance. A significant decrease (~63%) in the HF component was also noted during-kapalbhati as compared to the “pre-kapalbhati” values which shows decrease in parasympathetic tone. Thus, these results suggest that during-kapalbhati there is drastic increase of sympathetic tone whereas parasympathetic activity is reduced. We propose these changes in autonomic system control on heart are responsible for the myocardial ischemic attacks induced during kapalbhati in some individuals.
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