Published in JAMA Network Open, the study reveals promising progress toward predicting how patients with major depressive disorder (MDD) will respond to antidepressant medications using brain imaging and clinical data. The research demonstrated that brain connectivity patterns — specifically in the dorsal anterior cingulate cortex — could significantly improve predictions of treatment response across two large, independent clinical trials.Using machine learning models trained on clinical and neuroimaging data from more than 350 participants in two international trials — EMBARC in the U.S. and CANBIND-1 in Canada — the researchers evaluated whether their algorithms could reliably predict who would respond to common antidepressants like sertraline and escitalopram. They found that adding a brain connectivity marker to traditional clinical data (such as age, sex and baseline depression severity) significantly improved prediction performance across both studies.

Study finds a 18.5% reduction in sick leave following LEZ implementation in Greater London compared to areas  in England without low emission zones.

A study by Dartmouth researchers lays out a scientific framework for holding individual fossil fuel companies liable for the costs of climate change by tracing specific damages back to their emissions. The researchers use the tool to provide the first causal estimate of economic losses due to extreme heat driven by emissions. They report that carbon dioxide and methane output from just 111 companies cost the world economy $28 trillion from 1991 to 2020, with the five top-emitting firms linked to $9 trillion of those losses.