This meta-analysis provides preliminary evidence supporting the use of fecal microbiota transplantation (FMT) as a strategy to enhance the efficacy of immune checkpoint inhibitors (ICIs) in patients with advanced or refractory solid tumors.

Researchers have mapped the complex landscape of cytotoxic T lymphocytes (CTLs), revealing how their remarkable diversity and adaptability influence cancer immunotherapy outcomes. This comprehensive analysis identifies key biomarkers and regulatory pathways that could optimize personalized cancer treatments.

Researchers present a machine learning framework that forecasts individual mental health deterioration using limited, real-world data from wearables and smartphones. This enables personalized early interventions, shifting psychiatry from reactive to proactive care.

Researchers have successfully demonstrated that advanced generative AI (GenAI) models can accurately assess lung adenocarcinoma pathological features with remarkable precision. The comprehensive study shows Claude-3.5-Sonnet achieving 82.3% accuracy in cancer grading, potentially revolutionizing how pathologists diagnose and predict outcomes for lung cancer patients.

A research team from Southern Medical University has developed a machine learning-based gene model that predicts whether nasopharyngeal cancer (NPC) patients will benefit from radiotherapy. This predictive tool, called the NPC-RSS, was validated in both cell lines and patient samples. The model may guide personalized treatment decisions and improve survival outcomes for NPC patients.