The Effects of Antipsychotics on the Brain::What Have We Learnt from Structural Imaging of Schizophrenia? - A Systematic Review

Despite a large number of neuroimaging studies in schizophrenia reporting subtle brain abnormalities, we do not know to what extent such abnormalities reflect the effects of antipsychotic treatment on brain structure. We therefore systematically reviewed cross-sectional and follow-up structural brain imaging studies of patients with schizophrenia treated with antipsychotics. 30 magnetic resonance imaging (MRI) studies were identified, 24 of them being longitudinal and six cross-sectional structural imaging studies. In patients with schizophrenia treated with antipsychotics, reduced gray matter volume was described, particularly in the frontal and temporal lobes. Structural neuroimaging studies indicate that treatment with typical as well as atypical antipsychotics may affect regional gray matter (GM) volume. In particular, typical antipsychotics led to increased gray matter volume of the basal ganglia, while atypical antipsychotics reversed this effect after switching. Atypical antipsychotics, however, seem to have no effect on basal ganglia structure

Ancient androdioecy in the freshwater crustacean Eulimnadia

Among the variety of reproductive mechanisms exhibited by living systems, one permutation—androdioecy (mixtures of males and hermaphrodites)—is distinguished by its rarity. Models of mating system evolution predict that androdioecy should be a brief stage between hermaphroditism and dioecy (separate males and females), or vice versa. Herein we report evidence of widespread and ancient androdioecy in crustaceans in the genus Eulimnadia, based on observations of over 33 000 shrimp from 36 locations from every continent except Antarctica. Using phylogenetic, biogeographical and palaeontological evidence, we infer that androdioecy in Eulimnadia has persisted for 24–180 million years and has been maintained through multiple speciation events. These results suggest that androdioecy is a highly successful aspect of the life history of these freshwater crustaceans, and has persisted for orders of magnitude longer than predicted by current models of this rare breeding system

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024