Congenital stationary night blindness: An analysis and update of genotype–phenotype correlations and pathogenic mechanisms

Congenital stationary night blindness (CSNB) refers to a group of genetically and clinically heterogeneous retinal disorders. Seventeen different genes with more than 360 different mutations and more than 670 affected alleles have been associated with CSNB, including genes coding for proteins of the phototransduction cascade, those important for signal transmission from the photoreceptors to the bipolar cells or genes involved in retinoid recycling in the retinal pigment epithelium. This article describes the phenotypic characteristics of different forms of CSNB that are necessary for accurate diagnosis and to direct and improve genetic testing. An overview of classical and recent methods used to identify specific CSNB genotypes is provided and a meta-analysis of all previously published and novel data is performed to determine the prevalence of disease-causing mutations. Studies of the underlying molecular pathogenic mechanisms based on cell culture techniques and animal studies are outlined. The article highlights how the study of CSNB has increased understanding of the mechanisms of visual signalling in the retina, likely to prove important in developing future treatments for CSNB and other retinal disorders.     

Prevalence of shift work disorder among hospital personnel: A cross‐sectional study using objective working hour data

The prevalence of shift work disorder (SWD) has been studied using self‐reported data and the International Classification of Sleep Disorders, Second Edition (ICSD‐2) criteria. We examined the prevalence in relation to ICSD‐2 and ICSD‐3 criteria, work schedules and the number of non‐day shifts (work outside 06:00–18:00 hours) using objective working‐hours data. Secondly, we explored a minimum cut‐off for the occurrence of SWD symptoms. Hospital shift workers without (n = 1,813) and with night shifts (n = 2,917) and permanent night workers (n = 84) answered a survey (response rate 69%) on SWD and fatigue on days off. The prevalence of SWD was calculated for groups with ≥1, ≥3, ≥5 and ≥7 monthly non‐day shifts utilizing the working hours registry. ICSD‐3‐based SWD prevalence was 2.5%–3.7% (shift workers without nights), 2.6%–9.5% (shift workers with nights) and 6.0% (permanent night workers), depending on the cut‐off of non‐day shifts (≥7–1/month, respectively). The ICSD‐2‐based prevalence was higher: 7.1%–9.2%, 5.6%–33.5% and 16.7%, respectively. The prevalence was significantly higher among shift workers with than those without nights (p‐values <.001) when using the cut‐offs of ≥1–3 non‐day shifts. Shift workers with nights who had ≥3 days with ICSD‐3‐based SWD symptoms/month more commonly had fatigue on days off (49.3%) than those below the cut‐off (35.8%, p < .05). The ICSD‐3 criteria provided lower estimates for SWD prevalence than ISCD‐2 criteria, similarly to exclusion of employees with the fewest non‐day shifts. The results suggest that a plausible cut‐off for days with ICSD‐3‐based SWD symptoms is ≥3/month, resulting in 3%–6% prevalence of SWD.     

Validity,potential clinical utility and comparison of a consumer activity tracker and a research‐grade activity tracker in insomnia disorder II: Outside the laboratory

Accurate assessment of sleep can be fundamental for monitoring, managing and evaluating treatment outcomes within diseases. A proliferation of consumer activity trackers gives easy access to objective sleep. We evaluated the performance of a commercial device (Fitbit Alta HR) relative to a research‐grade actigraph (Actiwatch Spectrum Pro) in measuring sleep before and after a cognitive behavioural intervention in insomnia disorder. Twenty‐five individuals with DSM‐5 insomnia disorder (M = 50.6 ± 15.9 years) wore Fitbit and Actiwatch and completed a sleep diary during an in‐laboratory polysomnogram, and for 1 week preceding and following seven weekly sessions of cognitive‐behavioural intervention for insomnia. Device performance was compared for sleep outcomes (total sleep time, sleep latency, sleep efficiency and wake after sleep onset). The analyses assessed (a) agreement between devices across days and pre‐ to post‐treatment, and (b) whether pre‐ to post‐treatment changes in sleep assessed by devices correlated with clinical measures of change. Devices generally did not significantly differ from each other on sleep variable estimates, either night to night, in response to sleep manipulation (pre‐ to post‐treatment) or in response to changes in environment (in the laboratory versus at home). Change in sleep measures across time from each device showed some correlation with common clinical measures of change in insomnia, but not insomnia diagnosis as a categorical variable. Overall, the Fitbit provides similar estimates of sleep outside the laboratory to a research grade actigraph. Despite the similarity between Fitbit and Actiwatch performance, the use of consumer technology is still in its infancy and caution should be taken in its interpretation.     

Potential limitations in COVID-19 machine learning due to data source variability: A case study in the nCov2019 dataset

ObjectiveThe lack of representative coronavirus disease 2019 (COVID-19) data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, in which source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning.Materials and MethodsWe used the publicly available nCov2019 dataset, including patient-level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities.ResultsCases from the 2 countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting.ConclusionsData source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.     

Differences in Parent and Teacher Rating of Everyday Executive Function in Pediatric Brain Tumor Survivors

We aimed to compare executive function (EF) outcomes in pediatric brain tumor (BT) survivors compared with healthy children (HC) across multiple settings. This retrospective cross-sectional study of BT survivors and age- and gender-matched HC analyzed scale patterns of parent and teacher ratings of EF (Behavior Ratings of Executive Function; BRIEF). We also analyzed relationships between groups and raters (parent/teacher) and clinical elevations across EF domains on the BRIEF. Group differences in aspects of EF emerged from parent ratings in working memory (WM), while significant interactions from teacher ratings emerged on nearly all EF scales. Parents reported impaired cognitive/behavioral flexibility in the BT group four times more than parents of HC. Teachers rated survivors significantly more poorly as a group on the majority of EF domains, and indicated clinical impairment in cognitive/behavioral flexibility, emotional regulation, self-starting/initiation, WM, and planning and organization (P/O) four to ten times more often than the teachers of HC. Overall, teacher ratings of EF impairment in pediatric BT survivors were significantly greater than parent ratings, who reported far fewer EF problems. Possible explanations for inter-rater discrepancies include potential reporting bias/response shift in parents and/or differences in EF demands across settings.