High-normal blood glucose levels may be associated with decreased spatial perception in young healthy adults.

The negative effects of high normal glucose on cognitive function were previously reported in euglycemic individuals of middle age and the elderly population. This study aimed at examining the effect of baseline blood glucose levels on spatial ability, specifically verticality perception on the computerized rod and frame test (CRFT) in young healthy adults. 63 healthy male medical students (age range from 18-23 years), of whom 30 were non-fasting outside the month of Ramadan and 33 fasting during Ramadan of the year 2016, were recruited in order to create varying degrees of glycemia during which verticality perception was carried out. Baseline blood glucose reading was obtained prior to commencing the CRFT test. Blood glucose levels at the time of testing decreased as the duration between the last meal and testing increased. A blood glucose range of 62-117 mg/dl was achieved among participants for this study. Linear regression analysis showed that blood glucose level at testing correlated positively with all alignment spatial error parameters, indicating a probable reduction of spatial perception ability with higher blood glucose levels. These results are consistent with other cognitive studies in older healthy humans and emphasize the critical impact of early glucose dys-homeostasis on cognitive function. They also indicate that elevated blood glucose may affect cognitive functioning outside of the usual complications of diabetes

Sleep Loss Produces False Memories

People sometimes claim with high confidence to remember events that in fact never happened, typically due to strong semantic associations with actually encoded events. Sleep is known to provide optimal neurobiological conditions for consolidation of memories for long-term storage, whereas sleep deprivation acutely impairs retrieval of stored memories. Here, focusing on the role of sleep-related memory processes, we tested whether false memories can be created (a) as enduring memory representations due to a consolidation-associated reorganization of new memory representations during post-learning sleep and/or (b) as an acute retrieval-related phenomenon induced by sleep deprivation at memory testing. According to the Deese, Roediger, McDermott (DRM) false memory paradigm, subjects learned lists of semantically associated words (e.g., “night”, “dark”, “coal”,…), lacking the strongest common associate or theme word (here: “black”). Subjects either slept or stayed awake immediately after learning, and they were either sleep deprived or not at recognition testing 9, 33, or 44 hours after learning. Sleep deprivation at retrieval, but not sleep following learning, critically enhanced false memories of theme words. This effect was abolished by caffeine administration prior to retrieval, indicating that adenosinergic mechanisms can contribute to the generation of false memories associated with sleep loss

Effects of Time of Day and Sleep Deprivation on Motorcycle-Driving Performance

The aim of this study was to investigate whether motorcycle handling capabilities – measured by means of the efficiency of emergency manoeuvres – were dependent on prior sleep deprivation and time of day. Twelve male participants voluntarily took part in four test sessions, starting at 6 a.m., 10 a.m., 2 p.m., and 6 p.m., following a night either with or without sleep. Each test session comprised temperature and sleepiness measurements, before three different types of motorcycling tests were initiated: (1) stability in straight ahead riding at low speed (in “slow motion” mode and in “brakes and clutch” mode), (2) emergency braking and (3) crash avoidance tasks performed at 20 kph and 40 kph. The results indicate that motorcycle control at low speed depends on time of day, with an improvement in performance throughout the day. Emergency braking performance is affected at both speeds by time of day, with poorer performance (longer total stopping distance, reaction time and braking distance) in the morning, and also by sleep deprivation, from measurements obtained at 40 kph (incorrect initial speed). Except for a tendency observed after the sleepless night to deviate from the initial speed, it seems that crash avoidance capabilities are quite unaffected by the two disturbance factors. Consequently, some motorcycle handling capabilities (stability at low speed and emergency braking) change in the same way as the diurnal fluctuation observed in body temperature and sleepiness, whereas for others (crash avoidance) the participants were able to maintain their initial performance level despite the high levels of sleepiness recorded after a sleepless night. Motorcycle riders have to be aware that their handling capabilities are limited in the early morning and/or after sleep deprivation. Both these situations can increase the risk of falls and of being involved in a road accident

Prader–Willi syndrome and autism spectrum disorders: an evolving story

Prader–Willi syndrome (PWS) is well-known for its genetic and phenotypic complexities. Caused by a lack of paternally derived imprinted material on chromosome 15q11–q13, individuals with PWS have mild to moderate intellectual disabilities, repetitive and compulsive behaviors, skin picking, tantrums, irritability, hyperphagia, and increased risks of obesity. Many individuals also have co-occurring autism spectrum disorders (ASDs), psychosis, and mood disorders. Although the PWS 15q11–q13 region confers risks for autism, relatively few studies have assessed autism symptoms in PWS or directly compared social, behavioral, and cognitive functioning across groups with autism or PWS. This article identifies areas of phenotypic overlap and difference between PWS and ASD in core autism symptoms and in such comorbidities as psychiatric disorders, and dysregulated sleep and eating. Though future studies are needed, PWS provides a promising alternative lens into specific symptoms and comorbidities of autism

Sleep for Stroke Management and Recovery Trial (Sleep SMART): Rationale and methods

Rationale: Obstructive sleep apnea is common among patients with acute ischemic stroke and is associated with reduced functional recovery and an increased risk for recurrent vascular events. Aims and/or hypothesis: The Sleep for Stroke Management and Recovery Trial (Sleep SMART) aims to determine whether automatically adjusting continuous positive airway pressure (aCPAP) treatment for obstructive sleep apnea improves clinical outcomes after acute ischemic stroke or high-risk transient ischemic attack. Sample size estimate: A total of 3062 randomized subjects for the prevention of recurrent serious vascular events, and among these, 1362 stroke survivors for the recovery outcome. Methods and design: Sleep SMART is a phase III, multicenter, prospective randomized, open, blinded outcome event assessed controlled trial. Adults with recent acute ischemic stroke/transient ischemic attack and no contraindication to aCPAP are screened for obstructive sleep apnea with a portable sleep apnea test. Subjects with confirmed obstructive sleep apnea but without predominant central sleep apnea proceed to a run-in night of aCPAP. Subjects with use (≥4 h) of aCPAP and without development of significant central apneas are randomized to aCPAP plus usual care or care-as-usual for six months. Telemedicine is used to monitor and facilitate aCPAP adherence remotely. Study outcomes: Two separate primary outcomes: (1) the composite of recurrent acute ischemic stroke, acute coronary syndrome, and all-cause mortality (prevention) and (2) the modified Rankin scale scores (recovery) at six- and three-month post-randomization, respectively. Discussion: Sleep SMART represents the first large trial to test whether aCPAP for obstructive sleep apnea after stroke/transient ischemic attack reduces recurrent vascular events or death, and improves functional recovery

Sleep Deprivation Detection for Real-Time Driver Monitoring using Deep Learning

International audienceWe propose a non-invasive method to detect sleep deprivation by evaluating a short video sequence of a subject. Computer Vision techniques are used to crop the face from every frame and classify it (within a Deep Learning framework) into two classes: " rested " or " sleep deprived ". The system has been trained on a database of subjects recorded under severe sleep deprivation conditions. A prototype has been implemented in a low-cost Android device proving its viability for real-time driver monitoring applications. Tests on real world data have been carried out and show encouraging performances but also reveal the need of larger datasets for training