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.

A study published in PhotoniX Life presents a label-free method for mapping subcellular dynamics using wide-field interferometric scattering (iSCAT) microscopy and power spectral density (PSD) analysis. By fitting the pixel-wise PSD of iSCAT signals to an inverse-power-law relationship over 30–1,250 Hz, the researchers generated spectral exponent maps that visualize the characteristics and strength of subcellular motion. The method distinguished between mitotic and interphase cells, between live and apoptotic cells, and among thyroid cancer cell subtypes with increasing malignancy, highlighting a potential intrinsic optical marker for mechanobiology, cancer research, and stem cell assessment.

A joint research team from China Pharmaceutical University, Chinese Academy of Agricultural Sciences, National Engineering Research Center for the Emergency Drug, and Shandong Agricultural University has developed a novel multisite bridging‑mediated lateral flow immunoassay (mbLFIA) that enables ultrasensitive, enzyme‑free, visual on‑site detection of mosquito‑borne viruses such as chikungunya virus (CHIKV). The breakthrough, published in Targetome, addresses a critical global need for field‑deployable diagnostics in resource‑limited regions, where conventional nucleic acid tests rely on bioenzymes and bulky laboratory instruments.