Fibromyalgia is a complex disorder characterized primarily by chronic widespread pain, fatigue and other physical and cognitive symptoms. Although it affects millions of people worldwide, the underlying biological mechanisms are not yet fully understood. Now, a study by the University of Barcelona suggests that neuroimmune alterations in the central nervous system may play a key role in the development and progression of this condition. The findings, based on a comprehensive review of recent research in this field, help move beyond the view of fibromyalgia as a purely subjective condition and may open up new avenues for the diagnosis and treatment of this disorder.

Progesterone insensitive has long hindered fertility-preserving treatment for endometrial cancer. Now in Science Bulletin, researchers from Peking University People's Hospital and Peking University, in collaboration with Beihang University show that cholesteryl ester accumulation in the tumor microenvironment predicts response. Adding statins to standard therapy reverses progesterone insensitivity, significantly increasing the likelihood of both achieving endometrial cancer remission and preserving future fertility.

A comprehensive review published in Skin outlines the emergence of “Dermatology AI 2.0”, a fundamental transition from pattern recognition to cognitive and actionable intelligence. Built on four core pillars—causal inference, skin digital twins, predictive intervention, and distributed autonomous networks—this new paradigm enables AI to diagnose rare diseases 30% more accurately, predict disease flares with >90% accuracy, and deliver full-lifecycle skin health management. The review emphasizes that AI will not replace clinicians but will automate routine tasks, allowing physicians to focus on complex cases and patient care.

This study establishes a comprehensive three-level data standardization framework for integrating Western medicine (WM) and traditional Chinese medicine (TCM) electronic medical records in psoriasis research. It resolves the “data-rich, information-poor” paradox in dermatology and enables real-world evidence generation and AI-assisted clinical decision support for integrated therapies.