Accidental Hypothermia in a Swiss Alpine Trauma Centre-Not an Alpine Problem.

BACKGROUND Research in accidental hypothermia focuses on trauma patients, patients exposed to cold environments or patients after drowning but rarely on hypothermia in combination with intoxications or on medical or neurological issues. The aim of this retrospective single-centre cohort study was to define the aetiologies, severity and relative incidences of accidental hypothermia, methods of measuring temperature and in-hospital mortality. METHODS The study included patients ≥18 years with a documented body temperature ≤35 °C who were admitted to the emergency department (ED) of the University Hospital in Bern between 2000 and 2019. RESULTS 439 cases were included, corresponding to 0.32 per 1000 ED visits. Median age was 55 years (IQR 39-70). A total of 167 patients (38.0%) were female. Furthermore, 63.3% of the patients suffered from mild, 24.8% from moderate and 11.9% from severe hypothermia. Exposure as a single cause for accidental hypothermia accounted for 12 cases. The majority were combinations of hypothermia with trauma (32.6%), medical conditions (34.2%), neurological conditions (5.2%), intoxications (20.3%) or drowning (12.0%). Overall mortality was 22.3% and depended on the underlying causes, severity of hypothermia, age and sex

Causal network inference from gene transcriptional time-series response to glucocorticoids.

Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2768 genes and 31,945 directed edges (FDR ≤ 0.2). We validate inferred causal network edges using two external data sources: Overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS