Anthocyanins, the pigments that give blueberries their vivid coloration, are also recognized for their potential health benefits. 

The night-blooming cactus Epiphyllum oxypetalum is known for its brief yet powerfully fragrant flowers that bloom only for several hours at night. 

Preterm babies face greater health risks from the moment they are born. Their organs, immune systems, and metabolic pathways are not fully developed, making them more vulnerable to complications. Early identification of health risks becomes essential for improving survival and long-term development. A new study revealed that newborn genetic screening identified 94% of missed cases by the heel-prick test.

World Prematurity Day on Nov. 17 is an opportunity to raise awareness for the wider society that newborn genetic testing is a powerful new layer of protection. For preterm babies, who often present with overlapping symptoms, this early genetic insight gives clinicians a clearer path to faster, more targeted care.

A research team has developed a synthetic microbial consortium that completely reduces soluble uranium [U(VI)] to insoluble U(IV) within 48 hours, showing nearly twice the efficiency of a single-strain system. 

Researchers enhance the classic Zebiak–Cane model by refining key atmospheric parameters, improving the realism of ENSO simulation and offering a refined tool for ENSO research.

A new study published in Advances in Atmospheric Sciences has shed light on a previously overlooked but common atmospheric phenomenon: the transition zone (TZ) where clouds and aerosols blend together, making it difficult to tell where one ends and the other begins.

By developing the first fully-validated 3-D SSCPV-T collector design tool and using this to quantifying the performance of a proposed SSCPV-T collector, the study provides designers with a validated tool and a blueprint for next-generation, high-performance total-solar-utilisation cogeneration systems. The AgSiO₂-eg nanofluid configuration is particularly attractive for industrial users prioritising high-grade heat, while the water-based design remains cost-effective for medium-temperature applications. All modelling tools and optical/thermal correlations are being released open-source to accelerate commercial uptake.

The iMetaMed framework illustrates the integrative vision for medicine by dissolving disciplinary boundaries. Four major modules are highlighted: (1) Molecular & Computational Frontiers, represented by AlphaFold3 protein structure prediction (precision diagnostics), GeneCompass federated learning, and single-cell transcriptome integration; (2) AI-Enabled Clinical Translation, including AI-driven drug discovery, virtual cell modeling, and generative virtual staining; (3) Data Science & Infrastructure, featuring big data methodologies, dual-axis slicing, semantic dictionaries, and accelerated Biobank data extraction; and (4) Health Systems & Public Impact, encompassing telemedicine applications, open science, transparent peer review, multilingual dissemination, and diversity-oriented equity frameworks. At the core, iMetaMed envisions a seamless continuum from molecules to clinical practice, population health, and policy—transforming information abundance into actionable breakthroughs for global health.

Breast cancer is a highly heterogeneous malignancy among women worldwide. Traditional prognostic models relying solely on clinicopathological features offer limited predictive accuracy and lack molecular-level insights. Unlike such conventional approaches, this study integrates proteomic and clinical data within an interpretable deep learning framework to improve prognostic precision and biological interpretability. We aimed to develop a more reliable model to accurately predict the 5-year survival status of patients with breast cancer using multi-omics data. The model integrating proteomics and clinical features demonstrated superior performance (AUC = 0.8136) compared to other feature combination models. The optimized model with 13 key features (4 clinical features and 9 proteins) achieved an AUC of 0.864 with the precision of 0.970, the recall of 0.810, and F1-score of 0.883. SHapley Additive exPlanations analysis identified MPHOSPH10, EGFR, ARL3, KRT18, lymph node status, and HER2 status as the most influential features, while Kolmogorov–Arnold Network analysis provided explicit mathematical relationships between key contributors and prediction outcomes. Collectively, our interpretable multi-modal model demonstrates robust performance in predicting 5-year survival in breast cancer patients and offers mechanistic insights, thereby enhancing its potential for clinical translation through the development of an accessible prediction tool.