Diabetic cardiomyopathy (DCM), a major complication of diabetes mellitus, involves cardiac remodeling and dysfunction, often progressing to heart failure. Key pathological features include sarcoplasmic reticulum (SR) and mitochondrial calcium overload in cardiomyocytes, though the underlying mechanisms remain unclear. This study investigates the role of a cardiomyocyte-specific long noncoding RNA, Trdn-as, in DCM pathogenesis. Findings reveal that Trdn-as is significantly upregulated in cardiac tissues of diabetic mice and high glucose-treated cardiomyocytes. Experimental manipulation of Trdn-as in vivo and in vitro demonstrates its critical role in driving cardiac dysfunction and mitochondrial damage. Overexpression of Trdn-as in healthy mice induces DCM-like cardiac abnormalities, while silencing it in diabetic models alleviates structural and functional deficits, highlighting its potential as a therapeutic target.

A new analysis of polygenic scores for depression and bipolar disorder reveals these genetic markers consistently predict treatment outcomes across major mental health disorders with modest but meaningful effects. While the predictive power remains limited, researchers identify promising patterns that could eventually inform personalized psychiatric care when combined with clinical and environmental factors.

 Researchers from The University of Osaka found that both inhibitory and activating killer immunoglobulin-like receptors on natural killer (NK) cells were able to bind repetitive interspersed family proteins expressed on the surface of malaria-infected red blood cells. The role of these proteins in triggering both inhibitory and stimulatory responses from NK cells makes them extremely promising targets for the development of therapies and vaccines for malaria.

Osaka Metropolitan University researchers identified REDD2, a stress-responsive gene that damages insulin-producing pancreatic β-cells under metabolic stress. By disrupting insulin secretion, REDD2 contributes to the onset of type 2 diabetes. Suppressing this gene in mice helped preserve β-cell function and improve glucose control, offering a potential target for early diabetes intervention.

Stanford's Eric Sun has created sophisticated machine learning frameworks that measure biological aging at single-cell resolution, discovering cell types that can dramatically accelerate or slow aging in surrounding brain tissue. His recent Nature publication establishes new computational tools for understanding brain aging and developing targeted interventions against age-related cognitive decline.