Cancer diagnoses directly affect treatment decisions and prognosis and can even have life-altering consequences. Traditional pathological diagnosis primarily relies on pathologists examining and analysing tissue slides under a microscope and making judgements based on their professional experience. In recent years, the application of artificial intelligence (AI) in pathology diagnosis (pathology AI) has advanced rapidly, helping to improve diagnostic efficiency and support clinical decision-making. However, many existing AI models still lack a complete and verifiable mechanism to ensure the reliability of diagnostic outcomes, limiting the application of such technology in high-stakes clinical settings.

Bietti’s crystalline dystrophy (BCD) is a rare inherited retinal degenerative disease characterized by progressive night blindness, visual field constriction, and crystalline deposits in the retina and cornea. The disease is caused by mutations in CYP4V2, a gene involved in fatty acid metabolism. Traditionally, BCD has been viewed primarily as an ocular disorder. However, as CYP4V2-targeted gene therapies move into clinical exploration, a new question has become increasingly important: does BCD also involve measurable systemic metabolic changes, and could blood-based markers help monitor disease status and treatment-related metabolic safety?

A new study by researchers from the Eye and ENT Hospital of Fudan University, Shanghai Proton and Heavy Ion Center, and collaborators provides fresh evidence from plasma metabolomics. Using a family-controlled design, the team enrolled 36 genetically confirmed patients with BCD and 49 unaffected first-degree relatives as controls. This design helped reduce the confounding effects of genetic background and shared environmental differences, which can be especially important in rare inherited diseases. Through untargeted liquid chromatography–tandem mass spectrometry-based metabolomic profiling, the researchers found that BCD is associated not only with retinal degeneration, but also with systemic lipid metabolic rewiring, energy-related metabolic stress, and compensatory network changes.

JMIR Publications invites submissions to a new section on “Next-Generation Ambient Intelligence: “Invisible” Monitoring and AI Adoption for Aging-in-Place” in its open access journal JMIR Aging.

Insilico Medicine has announced the program for ARDD 2026, the 13th Aging Research & Drug Discovery Meeting, taking place October 1–3 at the David Rubenstein Treehouse at Harvard University in Boston.

The three-day program will convene global leaders from pharmaceutical and biotechnology companies, academia, clinical medicine, investment, artificial intelligence, scientific publishing and policy. Sessions will span aging biology, therapeutic discovery, clinical development, regulatory strategy, partnering and investment, reflecting the growing maturity of longevity biotechnology.