A new online platform, SMRT-Flames, can identify areas in need of land management in order to reduce future smoke exposures from uncontrolled fires. While most wildfire tools predict fire risk, SMRT-Flames explicitly considers smoke exposure across populations

An interdisciplinary George Mason University research team is breaking new ground in using artificial intelligence to support victims of interpersonal violence. Led by Kat Scafide and Janusz Wojtusiak of the College of Public Health and David Lattanzi of the College of Engineering and Computing, the Evidence-based AI Software for Injury Detection (EAS-ID) project has successfully completed Phase 1: development of a working prototype of a mobile app designed to accurately capture and document bruises. 

New research indicates that genetics may predict the effectiveness of anxiety and depression medications, potentially revolutionizing treatment approaches. Currently, nearly half of all patients don't respond to their initial prescription, leading to prolonged recovery. Researchers have developed a method using polygenic risk scores (PRS) from a simple genetic test to foresee which drugs will work best. While this technology has only been tested on genetic databases, clinical trials are anticipated. The goal is to develop an affordable and efficient test, allowing doctors to quickly select the most effective medication, thereby alleviating suffering much faster.

AI models have the potential not only to spot pancreatic cancer at an early stage, but also to predict the deadly disease’s prognosis, say scientists

A new study published in Nutrients offers new insight into omega-6 and whether it is truly the proinflammatory fatty acid many claim it to be.

Bronchiolitis obliterans syndrome (BOS) is a common complication in lung transplant patients. While molecular and pathological features have been well characterized, the influence of genetic factors on BOS development remains unexplored. Researchers from Pusan National University compared genetic variations in patient-derived lung tissues and found that the FCGBP gene is associated with BOS development and poor transplant outcomes, thereby, highlighting its role as a potential biomarker to predict transplant outcomes.