UCLA scientists are calling for a large-scale initiative to understand how human cells influence one another — a missing layer of biology that could reveal how cellular interactions drive disease and inform new therapies.

A deep learning system (DL-T1b) leveraging digital pathology achieved strong performance (AUC 0.910) for predicting lymph node metastasis in T1b gastric cancer. Integration with six clinical risk factors into a nomogram further boosted predictive accuracy, offering a precise tool for personalized treatment decisions. This study establishes proof-of-concept for deep learning-based pathomics in early gastric cancer risk stratification.

A new Editorial published in Translational Exercise Biomedicine (ISSN: 2942-6812), an official partner journal of International Federation of Sports Medicine (FIMS), argues that physical inactivity can no longer be described merely as a public-health crisis. Instead, the authors contend, it represents a persistent implementation failure of modern societies to align with fundamental human biology with consequences that strain global health systems, economic sustainability, and long-term human resilience. The editorial was authored by Yannis P. Pitsiladis, Daria Obratov, Fabio Pigozzi and Ugur Erdener.