Clinical prediction of sodium valproate-induced movement disorders in hospitalized patients: a nomogram-based model using real-world data.

Introduction: Drug-induced movement disorders (DIMDs) are often underrecognized and challenging to diagnose and manage in clinical practice. Sodium valproate (VPA), a widely prescribed antiepileptic drug, causes DIMDs. Predictive modeling based on electronic medical records and machine learning algorithms offers a promising approach to improve early identification of adverse drug reactions (ADRs) and enhance clinical safety.

Microbial and mineral interactions decouple litter quality from soil organic matter formation.

Current understanding of soil carbon dynamics suggests that plant litter quality and soil mineralogy control the formation of mineral-associated soil organic carbon (SOC). Due to more efficient microbial anabolism, high-quality litter may produce more microbial residues for stabilisation on mineral surfaces. To test these fundamental concepts, we manipulate soil mineralogy using pristine minerals, characterise microbial communities and use stable isotopes to measure decomposition of low- and high-quality litter and mineral stabilisation of litter-C.

Analysis of clinical characteristics and automatic monitoring of drug-induced arrhythmias in 167,546 inpatients.

Objective: The purpose of this study was to analyze the occurrence characteristics, clinical manifestations, medication distribution, and incidence of drug-induced arrhythmias in a real-world inpatient population.

Methods: According to the inclusion and exclusion criteria as well as the ADR evaluation criteria, we retrospectively evaluated hospitalized patients in 2019 using the arrhythmia module of the Adverse Drug Event Active Surveillance and Assessment System-II (ADE-ASAS-II). A detailed analysis was performed on the demographic data, ADR manifestations, and medication distribution of 2097 patients with drug-induced arrhythmias and QT interval prolongation.