Researchers at Osaka Metropolitan University developed models that classify X-ray images into specific body regions and simultaneously determine the imaging method and image orientation. Using these models, they successfully classified almost all data for use in deep-learning models.

Gastric (stomach) cancer remains one of the most common and deadly cancers in East Asia, including Korea. Yet despite its high prevalence, it has received far less molecular attention than colorectal cancer, which is more common in Western countries. As a result, many of today’s models of gastric cancer biology are still based on assumptions borrowed from colorectal cancer research — often with limited success when applied to patients.

One of the biggest unanswered questions has concerned the very first steps of gastric cancer development: how do early cancer cells survive and grow when they should not?

Under normal conditions, cells lining the stomach cannot grow independently. They rely on constant signals from their surrounding tissue — known as the microenvironment — to tell them when to divide, when to rest, and when to die. Losing this dependence is one of the defining features of cancer. But in gastric cancer, researchers have long struggled to explain how this transition occurs.

This problem has been tackled by a joint international research team led by Dr. LEE Ji-Hyun, Dr. KOO Bon-Kyoung, and Dr. LEE Heetak at the Center for Genome Engineering within the Institute for Basic Science (IBS), in partnership with the laboratories of Prof. CHEONG Jae-Ho and Prof. KIM Hyunki (Yonsei University College of Medicine) and Prof. Daniel E. STANGE (TU Dresden / University Hospital Carl Gustav Carus). The team has identified a previously unknown mechanism that allows early gastric cancer cells to become self-sufficient. The findings provide a new framework for understanding how stomach cancer begins — and point to potential new targets for treatment.

How consumers’ digital activity trackers could enable personalized health screening and early intervention for cardiovascular health and beyond.

Invisible pollutants at the nanoscale are quietly entering bodies and ecosystems, yet standard toxicology tools still treat living organisms like a black box. A new perspective article in the journal New Contaminants describes how cutting edge bioimaging is turning that black box into a transparent map, revealing where these particles go and how they may harm health.

A study led by biomedical scientists at the University of California, Riverside, has shown for the first time that a father’s exposure to microplastics can trigger metabolic dysfunctions in his offspring. The research, conducted using mouse models, highlights a previously unknown pathway through which environmental pollutants impact the health of future generations.

The International Society for Stem Cell Research (ISSCR) today announced the 10 most downloaded papers of 2025 from Stem Cell Reports, its peer-reviewed, open-access journal. Together, these highly read articles reflect the extraordinary scientific range of the field – from foundational mechanisms of pluripotency and differentiation to translational advances in regenerative medicine, disease modeling, and bioethics – and underscore the journal’s role as a trusted platform for impactful stem cell science.