Investigators have functionally linked the Alzheimer’s disease (AD) risk gene SORL1 to apolipoprotein E (ApoE) and clusterin, another apolipoprotein. The work, Tracy Young-Pearse told BioWorld, is part of an attempt to “try to understand different subtypes of Alzheimer’s disease.” It maps some of what Young-Pearse termed the “many molecular roads that lead to Alzheimer’s” – which, in turn, is the first step to setting up roadblocks. Young-Pearse is an associate professor in the Ann Romney Center for Neurologic Diseases at Brigham and Women’s Hospital and Harvard Medical School and co-leader of the Harvard Stem Cell Institute’s Nervous System Diseases Program. She is also the senior author of the paper describing the findings, which appeared online in Cell Reports on Aug. 22, 2023.

17-β-hydroxysteroid dehydrogenase type 10 (17-β-HSD10) catalyzes the turnover of steroids and neurosteroids, among other substrates, and is also a structural component of RNase P. 17-β-HSD10 is involved in the development of several pathologies, and considered a potential drug target for Alzheimer’s disease and some hormone-dependent cancers. A few 17-β-HSD10 inhibitors with either pyrazole, steroids, or benzothiazolylurea structure have been previously developed.

3Z ehf and Biotx.ai AG have established a strategic partnership to help advance the development of drugs for attention deficit hyperactivity disorder (ADHD).

The study of six types of mental illnesses in a thousand brain regions has demonstrated the differences between individuals within each disorder. Using magnetic resonance images (MRI), scientists from Monash University in Australia analyzed the brain changes and built individual maps for each psychiatric disorder. Their results revealed differences between people with the same diagnosis, which could help refine assessments.

Scientists have discovered that a small chemokine protein released by activated platelets, platelet factor 4 (PF 4), reduced neuroinflammation, and improved cognition in aged mice. The study was published on Aug. 16 in the online edition of Nature.

By using machine learning techniques to scour electronic health records, researchers have identified individuals who were likely to have binge eating disorder (BED) but had not received a formal diagnosis. Genomewide association studies including such patients enabled the investigators to identify several risk variants that were correlated with BED irrespective of body mass index (BMI), which covaries with BED and is a potential confounding factor.