Impact of the novel coronavirus (COVID-19) pandemic on sleep

Background: The COVID-19 pandemic has led to significant changes in daily routines and lifestyle worldwide and mental health issues have emerged as a consequence. We aimed to assess the presence of sleep disturbances during the lockdown in the general population. Methods: Cross-sectional, online survey-based study on adults living through the COVID-19 pandemic. The questionnaire included demographics and specific questions assessing the impact of the pandemic/ lockdown on sleep, daytime functioning and mental health in the general population. Identification of sleep pattern changes and specific sleep-related symptoms was the primary outcome, and secondary outcomes involved identifying sleep disturbances for predefined cohorts (participants reporting impact on mental health, self-isolation, keyworker status, suspected COVID-19 or ongoing COVID-19 symptoms). Results: In total, 843 participants were included in the analysis. The majority were female (67.4%), middle aged [52 years (40–63 years)], white (92.2%) and overweight to obese [BMI 29.4 kg/m2 (24.1–35.5 kg/m2)]; 69.4% reported a change in their sleep pattern, less than half (44.7%) had refreshing sleep, and 45.6% were sleepier than before the lockdown; 33.9% had to self-isolate, 65.2% reported an impact on their mental health and 25.9% were drinking more alcohol during the lockdown. More frequently reported observations specific to sleep were ‘disrupted sleep’ (42.3%), ‘falling asleep unintentionally’ (35.2%), ‘difficulties falling’/‘staying asleep’ (30.9% and 30.8%, respectively) and ‘later bedtimes’ (30.0%). Respondents with suspected COVID-19 had more nightmares and abnormal sleep rhythms. An impact on mental health was strongly associated with sleep-related alterations. Conclusions: Sleep disturbances have affected a substantial proportion of the general population during the COVID-19 pandemic lockdown. These are significantly associated with a self-assessed impact on mental health, but may also be related to suspected COVID-19 status, changes in habits and self-isolation

Correction to: A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer’s disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity

The IPDGC (The International Parkinson Disease Genomics Consortium) and EADB (Alzheimer Disease European DNA biobank) are listed correctly as an author to the article, however, they were incorrectly listed more than once

Correction to: A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer’s disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity

The IPDGC (The International Parkinson Disease Genomics Consortium) and EADB (Alzheimer Disease European DNA biobank) are listed correctly as an author to the article, however, they were incorrectly listed more than once

Effect of Buparlisib, a Pan-Class I PI3K Inhibitor, in Refractory Follicular and Poorly Differentiated Thyroid Cancer

International audienc

Language mapping with navigated repetitive TMS: Proof of technique and validation

OBJECTIVE: Lesion-based mapping of speech pathways has been possible only during invasive neurosurgical procedures using direct cortical stimulation (DCS). However, navigated transcranial magnetic stimulation (nTMS) may allow for lesion-based interrogation of language pathways noninvasively. Although not lesion-based, magnetoencephalographic imaging (MEGI) is another noninvasive modality for language mapping. In this study, we compare the accuracy of nTMS and MEGI with DCS. METHODS: Subjects with lesions around cortical language areas underwent preoperative nTMS and MEGI for language mapping. nTMS maps were generated using a repetitive TMS protocol to deliver trains of stimulations during a picture naming task. MEGI activation maps were derived from adaptive spatial filtering of beta-band power decreases prior to overt speech during picture naming and verb generation tasks. The subjects subsequently underwent awake language mapping via intraoperative DCS. The language maps obtained from each of the 3 modalities were recorded and compared. RESULTS: nTMS and MEGI were performed on 12 subjects. nTMS yielded 21 positive language disruption sites (11 speech arrest, 5 anomia, and 5 other) while DCS yielded 10 positive sites (2 speech arrest, 5 anomia, and 3 other). MEGI isolated 32 sites of peak activation with language tasks. Positive language sites were most commonly found in the pars opercularis for all three modalities. In 9 instances the positive DCS site corresponded to a positive nTMS site, while in 1 instance it did not. In 4 instances, a positive nTMS site corresponded to a negative DCS site, while 169 instances of negative nTMS and DCS were recorded. The sensitivity of nTMS was therefore 90%, specificity was 98%, the positive predictive value was 69% and the negative predictive value was 99% as compared with intraoperative DCS. MEGI language sites for verb generation and object naming correlated with nTMS sites in 5 subjects, and with DCS sites in 2 subjects. CONCLUSION: Maps of language function generated with nTMS correlate well with those generated by DCS. Negative nTMS mapping also correlates with negative DCS mapping. In our study, MEGI lacks the same level of correlation with intraoperative mapping; nevertheless it provides useful adjunct information in some cases. nTMS may offer a lesion-based method for noninvasively interrogating language pathways and be valuable in managing patients with peri-eloquent lesions

Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19

International audienceAbstract Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January–September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7–7.8%, p FDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7–10.5%, p FDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19–25%), cerebrovascular diseases (24%, 13–35%), nontraumatic intracranial hemorrhage (34%, 20–50%), encephalitis and/or myelitis (37%, 17–60%) and myopathy (72%, 67–77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease

International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries

International audienceAdditional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients

Evolving phenotypes of non-hospitalized patients that indicate long COVID

International audienceAbstract Background For some SARS-CoV-2 survivors, recovery from the acute phase of the infection has been grueling with lingering effects. Many of the symptoms characterized as the post-acute sequelae of COVID-19 (PASC) could have multiple causes or are similarly seen in non-COVID patients. Accurate identification of PASC phenotypes will be important to guide future research and help the healthcare system focus its efforts and resources on adequately controlled age- and gender-specific sequelae of a COVID-19 infection. Methods In this retrospective electronic health record (EHR) cohort study, we applied a computational framework for knowledge discovery from clinical data, MLHO, to identify phenotypes that positively associate with a past positive reverse transcription-polymerase chain reaction (RT-PCR) test for COVID-19. We evaluated the post-test phenotypes in two temporal windows at 3–6 and 6–9 months after the test and by age and gender. Data from longitudinal diagnosis records stored in EHRs from Mass General Brigham in the Boston Metropolitan Area was used for the analyses. Statistical analyses were performed on data from March 2020 to June 2021. Study participants included over 96 thousand patients who had tested positive or negative for COVID-19 and were not hospitalized. Results We identified 33 phenotypes among different age/gender cohorts or time windows that were positively associated with past SARS-CoV-2 infection. All identified phenotypes were newly recorded in patients’ medical records 2 months or longer after a COVID-19 RT-PCR test in non-hospitalized patients regardless of the test result. Among these phenotypes, a new diagnosis record for anosmia and dysgeusia (OR 2.60, 95% CI [1.94–3.46]), alopecia (OR 3.09, 95% CI [2.53–3.76]), chest pain (OR 1.27, 95% CI [1.09–1.48]), chronic fatigue syndrome (OR 2.60, 95% CI [1.22–2.10]), shortness of breath (OR 1.41, 95% CI [1.22–1.64]), pneumonia (OR 1.66, 95% CI [1.28–2.16]), and type 2 diabetes mellitus (OR 1.41, 95% CI [1.22–1.64]) is one of the most significant indicators of a past COVID-19 infection. Additionally, more new phenotypes were found with increased confidence among the cohorts who were younger than 65. Conclusions The findings of this study confirm many of the post-COVID-19 symptoms and suggest that a variety of new diagnoses, including new diabetes mellitus and neurological disorder diagnoses, are more common among those with a history of COVID-19 than those without the infection. Additionally, more than 63% of PASC phenotypes were observed in patients under 65 years of age, pointing out the importance of vaccination to minimize the risk of debilitating post-acute sequelae of COVID-19 among younger adults

Gene Expression Imputation Across Multiple Tissue Types Provides Insight Into the Genetic Architecture of Frontotemporal Dementia and Its Clinical Subtypes

Background: The etiology of frontotemporal dementia (FTD) is poorly understood. To identify genes with predicted expression levels associated with FTD, we integrated summary statistics with external reference gene expression data using a transcriptome-wide association study approach. Methods: FUSION software was used to leverage FTD summary statistics (all FTD: n = 2154 cases, n = 4308 controls; behavioral variant FTD: n = 1337 cases, n = 2754 controls; semantic dementia: n = 308 cases, n = 616 controls; progressive nonfluent aphasia: n = 269 cases, n = 538 controls; FTD with motor neuron disease: n = 200 cases, n = 400 controls) from the International FTD-Genomics Consortium with 53 expression quantitative loci tissue type panels (n = 12,205; 5 consortia). Significance was assessed using a 5% false discovery rate threshold. Results: We identified 73 significant gene–tissue associations for FTD, representing 44 unique genes in 34 tissue types. Most significant findings were derived from dorsolateral prefrontal cortex splicing data (n = 19 genes, 26%). The 17q21.31 inversion locus contained 23 significant associations, representing 6 unique genes. Other top hits included SEC22B (a gene involved in vesicle trafficking), TRGV5, and ZNF302. A single gene finding (RAB38) was observed for behavioral variant FTD. For other clinical subtypes, no significant associations were observed. Conclusions: We identified novel candidate genes (e.g., SEC22B) and previously reported risk regions (e.g., 17q21.31) for FTD. Most significant associations were observed in dorsolateral prefrontal cortex splicing data despite the modest sample size of this reference panel. This suggests that our findings are specific to FTD and are likely to be biologically relevant highlights of genes at different FTD risk loci that are contributing to the disease pathology

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

International audienceAbstract Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach