Journal Information
ISSN / EISSN : 01618105 / 15509109
Current Publisher: Oxford University Press (OUP) (10.1093)
Former Publisher: Associated Professional Sleep Societies (APSS) (10.5665)
Total articles ≅ 11,009
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Hassan S Dashti, Brian E Cade, Gerda Stutaite, Richa Saxena, Susan Redline, Elizabeth W Karlson
Published: 21 September 2020
Sleep; doi:10.1093/sleep/zsaa189

The publisher has not yet granted permission to display this abstract.
Daniel Vethe, Jan Scott, Morten Engstrøm, Øyvind Salvesen, Trond Sand, Alexander Olsen, Gunnar Morken, Hanne S Heglum, Kaia Kjørstad, Patrick M Faaland, et al.
Published: 21 September 2020
Sleep; doi:10.1093/sleep/zsaa194

Study objectives Blue-depleted lighting reduces the disruptive effects of evening artificial light on the circadian system in laboratory experiments, but this has not yet been shown in naturalistic settings. The aim of the current study was to test the effects of residing in an evening blue-depleted light environment (LE) on melatonin levels, sleep, neurocognitive arousal, sleepiness and potential side-effects. Methods The study was undertaken in a new psychiatric hospital unit where dynamic light sources were installed. All light sources in all rooms were blue-depleted in one half of the unit between 1830h and 0700h (melanopic lux range: 7 – 21, melanopic equivalent daylight illuminance range (M-EDI): 6-19, photopic lux range: 55-124), whereas the other had standard lighting (melanopic lux range: 30-70, M-EDI range: 27-63, photopic lux range: 64-136), but was otherwise identical. Twelve healthy adults resided for five days in each light LE in a randomized cross-over trial. Results Melatonin levels were less suppressed in the blue-depleted LE (15%) compared with the normal LE (45%) (p=0.011). DLMO was phase advanced more (1:20h) after residing in the blue-depleted LE than after the normal LE (0:46h) (p=0.008). Total sleep time was 8.1 minutes longer (p=0.032), REM sleep 13.9 minutes longer (p<0.001), and neurocognitive arousal was lower (p=0.042) in the blue-depleted LE. There were no significant differences in subjective sleepiness (p=0.16) or side-effects (p=0.09). Conclusion It is possible to create an evening light environment that has an impact on the circadian system and sleep without serious side-effects. This demonstrates the feasibility and potential benefits of designing buildings or hospital units according to chronobiological principles and provide a basis for studies in both non-clinical and clinical populations.
Lauriane Jugé, Jade Yeung, Fiona L Knapman, Peter G R Burke, Aimee B Lowth, Ken Zhi Gan, Elizabeth Brown, Jane E Butler, Danny J Eckert, Joachim Ngiam, et al.
Published: 21 September 2020
Sleep; doi:10.1093/sleep/zsaa196

The publisher has not yet granted permission to display this abstract.
Chenxi Song, Rui Zhang, Chunyue Wang, Rui Fu, Weihua Song, Kefei Dou, Shuang Wang
Published: 21 September 2020
Sleep; doi:10.1093/sleep/zsaa192

The publisher has not yet granted permission to display this abstract.
Krishna Melnattur, Leonie Kirszenblat, Ellen Morgan, Valentin Militchin, Blake Sakran, Denis English, Rushi Patel, Dorothy Chan, Bruno Van Swinderen, Paul J Shaw
Published: 21 September 2020
Sleep; doi:10.1093/sleep/zsaa197

Sleep loss and aging impair hippocampus-dependent Spatial Learning in mammalian systems. Here we use the fly Drosophila melanogaster to investigate the relationship between sleep and Spatial Learning in healthy and impaired flies. The Spatial Learning assay is modeled after the Morris Water Maze. The assay uses a ‘thermal maze’ consisting of a 5X5 grid of Peltier plates maintained at 36-37°C and a visual panorama. The first trial begins when a single tile that is associated with a specific visual cue is cooled to 25°C. For subsequent trials, the cold tile is heated, the visual panorama is rotated and the flies must find the new cold-tile by remembering its association with the visual cue. Significant learning was observed with two different wild-type strains – Cs and 2U, validating our design. Sleep deprivation prior to training impaired Spatial Learning. Learning was also impaired in the classic learning mutant rutabaga (rut); enhancing sleep restored learning to rut mutants. Further, we found that flies exhibited dramatic age-dependent cognitive decline in Spatial Learning starting at 20-24 days of age. These impairments could be reversed by enhancing sleep. Finally, we find that Spatial Learning requires dopaminergic signaling and that enhancing dopaminergic signaling in aged flies restored learning. Our results are consistent with the impairments seen in rodents and humans. These results thus demonstrate a critical conserved role for sleep in supporting Spatial Learning, and suggest potential avenues for therapeutic intervention during aging.
Evan A Winiger, Jarrod M Ellingson, Claire L Morrison, Robin P Corley, Joëlle A Pasman, Tamara L Wall, Christian J Hopfer, John K Hewitt
Published: 16 September 2020
Sleep; doi:10.1093/sleep/zsaa188

The publisher has not yet granted permission to display this abstract.
Eysteinn Finnsson, Guðrún H Ólafsdóttir, Dagmar L Loftsdóttir, Sigurður Æ Jónsson, Halla Helgadóttir, Jón S Ágústsson, Scott A Sands, Andrew Wellman
Published: 15 September 2020
Sleep; doi:10.1093/sleep/zsaa168

Sleep apnea is caused by several endophenotypic traits, namely pharyngeal collapsibility, poor muscle compensation, ventilatory instability (high loop gain), and arousability from sleep (low arousal threshold). Measures of these traits have shown promise for predicting outcomes of therapies (e.g. oral appliances, surgery, hypoglossal nerve stimulation, CPAP, and pharmaceuticals), which may become an integral part of precision sleep medicine. Currently the methods Sands et al. [1] developed for endotyping sleep apnea from polysomnography (PSG) are embedded in the original authors’ code, which is computationally expensive and requires technological expertise to run. We present a re-implementation and validation of the integrity of the original authors’ code by reproducing the endo-Phenotype Using Polysomnography (PUP) method of Sands et al. [1, 2] The original MATLAB methods were reprogrammed in Python; efficient methods were developed to detect breaths, calculate normalized ventilation (moving time-average), and model ventilatory drive (intended ventilation). The new implementation (PUPpy) was validated by comparing the endotypes from PUPpy with the original PUP results. Both endotyping methods were applied to 38 manually scored polysomnographic studies. Results of the new implementation were strongly correlated with the original (p<10 -6 for all): ventilation at eupnea V̇passive (ICC=0.97), ventilation at arousal onset V̇active (ICC=0.97), loop-gain (ICC=0.96), and arousal threshold (ICC=0.90). We successfully implemented the original method by Sands [1, 2] providing further evidence of its integrity. Additionally, we created a cloud-based version for scaling up sleep apnea endotyping that can be used more easily by a wider audience of researchers and clinicians.
T Rosinvil, J Bouvier, J Dubé, A Lafrenière, M Bouchard, J Cronier, N Gosselin, J Carrier, J M Lina
Published: 15 September 2020
Sleep; doi:10.1093/sleep/zsaa186

The publisher has not yet granted permission to display this abstract.
Julio Fernandez-Mendoza, Elizaveta Bourchtein, Susan Calhoun, Kristina Puzino, Cynthia K Snyder, Fan He, Alexandros N Vgontzas, Duanping Liao, Edward Bixler
Published: 15 September 2020
Sleep; doi:10.1093/sleep/zsaa187

The publisher has not yet granted permission to display this abstract.
Sabra M Abbott, Jin Choi, John Wilson, Phyllis C Zee
Published: 14 September 2020
Sleep; doi:10.1093/sleep/zsaa184

The publisher has not yet granted permission to display this abstract.
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