Researchers at Emory University demonstrate that psilocybin’s metabolite psilocin delays cellular senescence and improves survival in aged mice by upregulating SIRT1, reducing oxidative stress, and preserving telomere length, suggesting a new geroprotective strategy.

Key points

• Psilocin extends human fibroblast lifespan by up to 57% via delayed replicative senescence and increased population doublings.
• Treatment elevates SIRT1, reduces Nox4-driven oxidative stress, activates Nrf2, and preserves telomere length in vitro.
• Monthly oral psilocybin dosing in aged C57BL/6J mice boosts survival from 50% to 80% over ten months and improves fur quality.

Why it matters: This work suggests psychedelics may become a novel geroprotective intervention, offering a chemical approach to slow aging hallmarks, preserve tissue function, and treat age-related diseases.

Alfaisal University researchers evaluate twelve machine learning algorithms—including logistic regression, random forests, and neural networks—on UCI heart disease data, assessing how preprocessing steps like standardization and SMOTE affect accuracy, F1 score, and other key metrics.

Key points

• CatBoost achieves highest accuracy (89.71%) and lowest logloss (0.2735) in heart disease prediction.
• SMOTE balancing prevents class bias, improving recall for patients with heart disease.
• Comparison of feature scaling methods reveals optimal preprocessing pipelines for ML convergence and performance.

Why it matters: This systematic AI benchmark identifies optimal preprocessing and modeling strategies for reliable, scalable heart disease prediction in clinical settings.

A team at Huazhong University of Science and Technology develops a machine‐learning pipeline that integrates KNN–MLP imputation, extreme gradient boosting with recursive feature elimination, and error‐correcting output codes to forecast hemoglobin concentration 30 days post‐kidney transplantation, aiming to guide clinical risk assessment.

Key points

• KNN–MLP fusion imputation leverages both vertical and horizontal data correlations to accurately fill missing clinical values.
• RFE‐optimized XGBoost selects 25 critical preoperative and postoperative variables, maintaining accuracy within 0.1% of the full model.
• ECOC‐enhanced extreme gradient boosting boosts multiclass hemoglobin classification accuracy to 87.22% and micro‐average AUC to 90.42% on test data.

Why it matters: By integrating advanced imputation and error‐correcting codes into gradient boosting, this approach significantly advances clinical risk forecasting, paving the way for personalized post‐transplant care and potentially improved patient outcomes.

Longevity Method, a pioneering health and wellness firm, releases a suite of five anti-aging formulations designed to target core aging mechanisms. By leveraging peer-reviewed ingredients such as NMN, urolithin A, and resveratrol, these supplements aim to replenish NAD+ levels, clear senescent cells, and enhance mitochondrial function. Each product addresses specific health goals—ranging from cellular repair and increased vitality to improved sleep and hormonal balance—offering a comprehensive approach to extending healthspan.

Key points

• NMN+ combines NMN, urolithin A, Ca-AKG, and TMG to replenish NAD+ and support mitochondrial energy.
• Live Longer Booster uses trans-resveratrol, quercetin, fisetin, and spermidine to activate sirtuins and clear senescent cells.
• Specialized blends include adaptogens and phytoestrogens for sleep optimization, muscle recovery, and hormonal balance.

Why it matters: By targeting NAD+ restoration and senescent cell clearance, these supplements offer a novel, science-backed approach to extending healthspan.

Researchers integrate a brain-computer interface system (BCIS) with machine learning algorithms to track autonomic signals in dysautonomia patients. The BCIS captures neural and cardiovascular data, the AI model identifies early warning patterns, and the platform alerts users to intervene, reducing the risk of sudden fainting events.

Key points

• Non-invasive EEG sensors and heart rate monitors record neural and cardiovascular signals.
• Machine learning algorithms analyze personalized data streams to identify pre-syncopal biomarkers.
• The integrated BCIS platform delivers early alerts, reducing fainting episodes by approximately 80% in patient trials.

Why it matters: This AI-integrated BCIS offers proactive, personalized management of autonomic disorders, potentially reducing emergencies and improving patient autonomy.