The Institute for Basic Science demonstrates that astrocytic enzyme SIRT2 catalyzes excessive GABA production and contributes to memory impairment in Alzheimer’s. Using molecular and imaging analyses in transgenic mice, the team shows that inhibiting astrocytic SIRT2 attenuates GABA release and restores working memory performance, providing a targeted strategy for modulating neuroinflammation-driven cognitive decline.

Key points

• Identification of SIRT2 and ALDH1A1 as key enzymes driving astrocytic GABA overproduction.
• Selective inhibition of astrocytic SIRT2 reduces GABA release and rescues Y-maze working memory deficits.
• Elevated SIRT2 expression confirmed in both Alzheimer’s mouse model astrocytes and human patient brain tissue.
• Study combines molecular analysis, microscopic imaging, and electrophysiology to elucidate enzyme roles.
• Decoupling GABA synthesis from H₂O₂ generation enables precise targeting of inhibitory signaling.

Why it matters: This study shifts the paradigm from neuron-centric to glia-mediated mechanisms in Alzheimer’s, highlighting SIRT2 as a selective modulator of inhibitory signaling. By decoupling GABA from oxidative stress, it opens paths to precision therapies aimed at astrocyte reactivity, potentially improving cognitive outcomes with fewer off-target effects.

A team at Johns Hopkins University employs high-content imaging paired with machine learning to categorize three unique fibroblast senescence subtypes, revealing differential drug responses. This classification paves the way for tailored senolytic interventions that selectively clear pro-inflammatory cells in aging skin.

Key points

• Identification of three distinct senescent fibroblast subtypes via automated imaging
• Use of 87 morphological parameters and machine learning classification
• High prevalence of the C10 subtype in donors over 50 years old
• Differential drug responses across subtypes inform targeted senolytic design
• Data derived from fibroblasts in the Baltimore Longitudinal Study samples

Why it matters: This finding reveals heterogeneity within senescent cell populations, enabling precision targeting of disease-driving subtypes and reducing off-target effects. By moving beyond broad-spectrum senolytic approaches, it offers a transformative strategy for personalized anti-aging interventions and improved patient outcomes.

Think of a radar scanning for threats before they appear: University of Southampton and Xgenera’s AI test mines microRNA from 10 drops to detect and pinpoint 12 cancers. In NHS trials, this approach could replace invasive biopsies and streamline early treatment.

Key points

• AI-powered blood test detects 12 common cancers with 99% accuracy from just 10 drops
• miONCO-Dx locates tumor origin and reduces need for invasive diagnostics
• NHS clinical trial involves 8,000 patients supported by £2.4 million funding