A research team at NOVA University Lisbon performs a comprehensive scoping review of supervised ML frameworks—including XGBoost, Random Forest, and LASSO—leveraging electronic health record datasets to predict 30- and 90-day heart failure hospitalisation and readmission risks, emphasizing ensemble methods and the current lack of economic impact assessments.

Key points

• Ensemble algorithms (XGBoost, CATBOOST) achieved top predictive performance with mean AUC up to 0.88 for unspecified-period heart failure risk.
• EHR-derived datasets across 13 countries provided clinical, demographic, and utilization variables for 30- and 90-day risk modelling.
• No reviewed studies included economic evaluations, indicating a critical gap for assessing cost-effectiveness before clinical deployment.

Why it matters: This synthesis underscores ensemble ML's potential to refine heart failure risk stratification and highlights gaps in cost-effectiveness evaluations crucial for clinical adoption.

A meta-analysis published in Aging Cell unites data from eight vertebrate species to demonstrate that rapamycin achieves lifespan extension equivalent to severe caloric restriction by inhibiting the mTOR pathway. This finding highlights rapamycin’s potential as a practical anti-aging intervention.

Key points

• Meta-analysis of 167 studies across eight vertebrate species shows rapamycin extends lifespan on par with severe caloric restriction.
• Rapamycin inhibits the mTOR pathway, mimicking nutrient scarcity to activate cellular repair, autophagy, and stress-resistance programs.
• Intermittent dosing schedules and development of rapalogs aim to retain longevity effects while reducing immunosuppressive side effects in healthy humans.

Why it matters: Demonstrating that a drug can reliably mimic the gold-standard dietary restriction effect marks a paradigm shift towards feasible pharmaceutical longevity therapies.

Scientists at the MRC Laboratory of Medical Sciences and Max Planck Institute for Biology of Ageing demonstrate that ubiquitous catalase overexpression in female Drosophila induces an oxidizing thiol shift, activating autophagy via redox control of Atg4a. This targeted redox modulation, independent of dietary restriction, extends healthspan and median lifespan by 10–15% in flies.

Key points

• Ubiquitous catalase overexpression in female Drosophila white Dahomey flies induces a ~10–15% lifespan extension.
• Redox proteomics (OxICAT) reveals a global oxidizing shift in cysteine thiol oxidation, triggering autophagy.
• Redox regulation of Atg4a via Cys102 oxidation is required for autophagy induction and longevity benefits.

Why it matters: This study reveals a direct link between redox signaling and autophagy in vivo, offering a precise, non‐invasive strategy to enhance longevity via targeted redox modulation.

A clinical team demonstrates that high-dose vitamin D3 paired with N-acetylcysteine synergistically reduces cellular senescence markers by modulating inflammatory cytokines and enhancing antioxidant defenses in aging populations.

Key points

• Combination of 5000 IU vitamin D3 and 600 mg NAC daily reduced SA-β-gal activity by 2.52% in older adults.
• NAC boosted glutathione production and inhibited NF-κB signaling, enhancing antioxidant defenses in immune cells.
• Iranian randomized controlled trial with four arms over eight weeks showed superior p16 gene expression reduction in the D5N group.

Why it matters: This synergistic supplementation approach could reshape anti-aging interventions by targeting multiple cellular aging pathways with safe, cost-effective compounds.

A team from the University of Edinburgh’s Centre for Electronic Frontiers employs AI-driven workflows across five key pillars—materials discovery, device design, circuit synthesis, testing, and digital twin modeling—to accelerate nanoelectronics development, boost yield, and promote greener manufacturing processes.

Key points

• AI-driven materials discovery predicts novel, sustainable nanoelectronic compounds using machine learning surrogate models.
• Advanced neural networks optimize nano-device architectures and automate circuit synthesis, improving performance and reducing design iterations.
• Physics-informed digital twins enable real-time device modeling and predictive maintenance across the electronics supply chain.

Why it matters: This integrated AI framework reshapes nanoelectronics by cutting development cycles, driving sustainable manufacturing, and enabling next-generation device performance.