Integrative analyses highlight functional regulatory variants associated with neuropsychiatric diseases.

Noncoding variants of presumed regulatory function contribute to the heritability of neuropsychiatric disease. A total of 2,221 noncoding variants connected to risk for ten neuropsychiatric disorders, including autism spectrum disorder, attention deficit hyperactivity disorder, bipolar disorder, borderline personality disorder, major depression, generalized anxiety disorder, panic disorder, post-traumatic stress disorder, obsessive-compulsive disorder and schizophrenia, were studied in developing human neural cells. Integrating epigenomic and transcriptomic data with massively parallel reporter assays identified differentially-active single-nucleotide variants (daSNVs) in specific neural cell types. Expression-gene mapping, network analyses and chromatin looping nominated candidate disease-relevant target genes modulated by these daSNVs. Follow-up integration of daSNV gene editing with clinical cohort analyses suggested that magnesium transport dysfunction may increase neuropsychiatric disease risk and indicated that common genetic pathomechanisms may mediate specific symptoms that are shared across multiple neuropsychiatric diseases.

Reduced Mortality With Ondansetron Use in SARS-CoV-2-Infected Inpatients.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has led to a surge in clinical trials evaluating investigational and approved drugs. Retrospective analysis of drugs taken by COVID-19 inpatients provides key information on drugs associated with better or worse outcomes. METHODS: We conducted a retrospective cohort study of 10 741 patients testing positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection within 3 days of admission to compare risk of 30-day all-cause mortality in patients receiving ondansetron using multivariate Cox proportional hazard models. All-cause mortality, length of hospital stay, adverse events such as ischemic cerebral infarction, and subsequent positive COVID-19 tests were measured. RESULTS: Administration of ≥8 mg of ondansetron within 48 hours of admission was correlated with an adjusted hazard ratio for 30-day all-cause mortality of 0.55 (95% CI, 0.42-0.70; P < .001) and 0.52 (95% CI, 0.31-0.87; P = .012) for all and intensive care unit-admitted patients, respectively. Decreased lengths of stay (9.2 vs 11.6; P < .001), frequencies of subsequent positive SARS-CoV-2 tests (53.6% vs 75.0%; P = .01), and long-term risks of ischemic cerebral ischemia (3.2% vs 6.1%; P < .001) were also noted. CONCLUSIONS: If confirmed by prospective clinical trials, our results suggest that ondansetron, a safe, widely available drug, could be used to decrease morbidity and mortality in at-risk populations.

A Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Prediction Model From Standard Laboratory Tests.

BACKGROUND: With the limited availability of testing for the presence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and concerns surrounding the accuracy of existing methods, other means of identifying patients are urgently needed. Previous studies showing a correlation between certain laboratory tests and diagnosis suggest an alternative method based on an ensemble of tests. METHODS: We have trained a machine learning model to analyze the correlation between SARS-CoV-2 test results and 20 routine laboratory tests collected within a 2-day period around the SARS-CoV-2 test date. We used the model to compare SARS-CoV-2 positive and negative patients. RESULTS: In a cohort of 75 991 veteran inpatients and outpatients who tested for SARS-CoV-2 in the months of March through July 2020, 7335 of whom were positive by reverse transcription polymerase chain reaction (RT-PCR) or antigen testing, and who had at least 15 of 20 lab results within the window period, our model predicted the results of the SARS-CoV-2 test with a specificity of 86.8%, a sensitivity of 82.4%, and an overall accuracy of 86.4% (with a 95% confidence interval of [86.0%, 86.9%]). CONCLUSIONS: Although molecular-based and antibody tests remain the reference standard method for confirming a SARS-CoV-2 diagnosis, their clinical sensitivity is not well known. The model described herein may provide a complementary method of determining SARS-CoV-2 infection status, based on a fully independent set of indicators, that can help confirm results from other tests as well as identify positive cases missed by molecular testing.