Researchers at Boston Children's Hospital engineer an enzymatic stabilization method for the telomerase RNA component (TERC), creating synthetic eTERC molecules that integrate into human stem cells. A single exposure increases telomere length for roughly two months without altering endogenous processes. This targeted telomere extension platform could enable new treatments for telomere biology disorders characterized by accelerated cellular aging.

Key points

• Enzymatic stabilization produces engineered telomerase RNA component (eTERC) for targeted telomere extension.
• A single eTERC exposure extends telomere length in human stem cells equivalent to years of replicative capacity.
• eTERC integration preserves endogenous telomerase regulation, avoiding disruption of normal cell processes.

Why it matters: This strategy opens a new therapeutic avenue for telomere biology disorders by providing a non-disruptive, single-dose boost to cellular health and lifespan potential.

Hebei General Hospital researchers develop a radiomics-based machine learning pipeline to preoperatively predict spread through air spaces (STAS) in lung adenocarcinoma. They segment tumor regions on CT images, extract quantitative texture, shape, and intensity features, and apply LASSO and classifiers including a ResNet50 deep learning network. The model achieves AUCs up to 0.918, offering a non-invasive tool to guide surgical planning.

Key points

• Extracted 3D CT radiomic features (texture, shape, intensity) screened via Mann–Whitney U, Spearman filtering, and LASSO reduction.
• Combined clinical markers (CEA level, FEV1/FVC) with radiomics in a nomogram achieving AUC 0.878 for STAS prediction.
• Employed ResNet50-based deep learning to derive 2D features, boosting classification AUC to 0.918 in machine learning models.

Why it matters: This AI-driven radiomics approach enables non-invasive, accurate preoperative risk stratification for lung adenocarcinoma, improving surgical planning and patient outcomes.

George Church’s 2022 PNAS paper on using cytomegalovirus as a gene therapy vector for anti-aging has been retracted due to image irregularities flagged by experts. The incident underscores the need for rigorous data backup and post-publication review in longevity research.

Key points

• Retraction of 2022 PNAS study on CMV-based anti-aging gene therapy due to image duplication and data gaps.
• Image sleuths flagged oversaturated blots and duplicated panels, revealing inadequate raw data backups.
• Incident highlights tensions between rapid commercialization of longevity therapies and the need for rigorous verification.

Why it matters: This retraction underscores the necessity for robust data integrity and transparency to sustain trust and progress in longevity therapeutics.

Didier Coeurnelle, co-chair of Heales and board member of the International Longevity Alliance, examines the latest advancements and obstacles in longevity research. He surveys lifestyle and environmental factors, healthcare disparities, and promising preventive and biotechnological approaches to support healthier aging globally.

Key points

• Income, education, and environmental disparities drive unequal access to healthy aging resources.
• Lifestyle factors and preventive healthcare serve as primary enablers for extending healthspan.
• Emerging therapies—senolytics, NAD+ boosters, and drug repurposing—offer targeted approaches to combat cellular aging.

Why it matters: By mapping barriers and enablers, this analysis informs integrative strategies to extend human healthspan and guide future longevity interventions.

A team at Beijing Chaoyang Hospital builds and compares five supervised machine learning algorithms using clinical, echocardiographic, and hemodynamic features. They identify six key predictors via LASSO, train models with logistic regression, SVM, random forest, XGBoost, and decision tree, and use SHAP to interpret the best model’s decisions in predicting BPA outcomes.

Key points

• Six predictors selected by LASSO: occlusive lesion proportion, TAPSE/PASP, 6MWD, RVESA, TR severity, PVR.
• Logistic regression with L2 regularisation outperforms other ML models, achieving test AUC of 0.865, accuracy 0.848, sensitivity 0.950.
• SHAP analysis identifies occlusive lesion proportion as the most influential feature driving BPA response predictions.

Why it matters: A reliable ML tool for preoperative BPA response prediction can enhance patient selection, reduce procedural risks, and improve outcomes in CTEPH management.

Researchers at Chongqing University of Posts and Telecommunications and University of Otago develop two quantum granular-ball generation methods—iterative splitting and fixed-split—to accelerate KNN classification. They map classical data into qubit rotation angles, compute fidelities with swap tests and QADC, and leverage quantum minimum search to cluster samples into granular-balls, achieving significant time complexity reductions over classical approaches.

Key points

• Iterative splitting algorithm encodes dataset and center points into qubit rotation angles using QRAM and CRY gates for efficient data preparation.
• Fidelities between samples and centers are computed via swap test and abs-QADC with phase estimation to embed distance information in digital registers.
• Quantum minimum search assigns each data point to its nearest granular-ball center, achieving O((log^2 N) N^1/4) depth for the fixed-split method.

Why it matters: This quantum granular-ball method reduces KNN clustering complexity, paving the way for scalable, high-speed quantum machine learning on large datasets.

DataM Intelligence employs quantitative and qualitative analysis to project significant AI market expansion from 2024 to 2031, fueled by strategic partnerships like NVIDIA–Oracle and Microsoft–NVIDIA integration across multiple sectors.

Key points

• Market research forecasts significant global AI CAGR from 2024 to 2031 based on quantitative analysis.
• Major partnerships: NVIDIA–Oracle for accelerated computing and Microsoft–NVIDIA for healthcare AI advancement.
• Google’s Lumiere introduces generative AI for video creation, expanding AI’s creative applications.

Local governments in Shenzhen, Beijing and Shanghai intensify competition to attract AI investment and talent. They establish dedicated AI agencies, offer up to billions of yuan in subsidies, and support applications from humanoid robotics and autonomous vehicles to digital humans at transit hubs. Leveraging academic research institutes, industrial parks and financial markets, these cities aim to build global AI ecosystems that translate technological advances into commercial and public-sector deployments.

Key points

• Shenzhen establishes the world’s first district-level AI Robotics Administration and offers up to 4.5 billion yuan in incentives for humanoid robot development and deployment.
• Beijing consolidates over 2,400 registered AI firms within the Zhongguancun Science Park, backed by national research institutes and driving a nearly 350 billion yuan core AI industry.
• Shanghai deploys a city-wide fully-optical network and allocates 1 billion yuan in targeted subsidies to integrate AI solutions across finance, logistics and manufacturing sectors.

Why it matters: China’s multi-city AI race demonstrates a model for public-private collaboration in scaling AI technologies, potentially accelerating global adoption and innovation.