Depression is a heterogeneous mental illness with substantial personal and societal burdens, yet its diagnosis still relies heavily on subjective assessments. Recent advances in blood-based metabolomics have opened new avenues for identifying objective biomarkers associated with depressive symptoms. This review highlights key findings from multicenter clinical and translational research that demonstrate reproducible associations between specific plasma metabolites—such as 3-hydroxybutyrate, betaine, citrate, creatinine, and γ-aminobutyric acid (GABA)—and the severity of depressive states. Several metabolites also appear to be linked to distinct symptom domains, including suicidal ideation (SI), a critical risk factor for self-harm. Notably, combinations of citrate and kynurenine have shown potential for SI severity estimation through machine learning models, suggesting a basis for minimally invasive risk stratification. In parallel, rodent models of stress-induced depression reveal consistent alterations in tryptophan and alanine metabolism, providing insight into possible causal mechanisms involving neurotransmitter biosynthesis and intestinal absorption under stress. Personality-based biotyping and artificial intelligence further refine the stratification of depressive phenotypes, offering prospects for more personalized diagnostics. Although methodological standardization and broader validation remain necessary, accumulating evidence supports the clinical utility of blood metabolomics as a complementary tool for early detection, subtype classification, and suicide risk assessment in depression.

A research team presents XFruitSeg, a deep learning model designed to accurately segment three-dimensional computed tomography (CT) images of plant fruits.

In response to the growing need for reporting standards for evaluating artificial intelligence (AI) chatbot health advice studies for clinical purposes, researchers created the Chatbot Assessment Reporting Tool (CHART) so stakeholders can interpret results with confidence.

Manipulating the endocannabinoid signaling system in prenatal mice leads to long-lasting neural and genetic changes that may lead to social deficits.

Pea-sized brains grown in a lab have for the first time revealed the unique way neurons might misfire due to schizophrenia and bipolar disorder, psychiatric ailments that affect millions of people worldwide but are difficult to diagnose because of the lack of understanding of their molecular basis. The research only involved 12 patients, but the findings will likely have real-world, clinical application as they could be the beginning of an important testbed for psychiatric drug therapies.