CardioNVT is an end-to-end deep learning platform for rapid, immunostaining-free assessment of cardiomyocyte nuclear ploidy in situ. Using only DAPI-stained heart sections, it segments cardiomyocyte nuclei, tracks them across z-stack planes, reconstructs 3D nuclear volumes, and infers ploidy from volume measurements, enabling scalable studies of cardiac development, remodeling, and repair.

A new study in Science Bulletin presents DVSTP, a deep learning system that integrates pathology images with spatial transcriptomics and proteomics to map intra-tumor heterogeneity. DVSTP predicts molecular profiles from routine pathology slides, making spatial multi-omics more accessible. Whole–tumor 3D reconstruction reveals that SRSF6 drives immune exclusion and is associated with poor clinical outcomes.

Large language models and autonomous agents have advanced rapidly, showing broad promise in medical imaging analysis, clinical diagnosis, and treatment planning. However, most existing medical AI systems still rely primarily on pre-trained knowledge and fixed workflows, making it difficult to learn continuously from long-term clinical feedback, patient outcomes, and prior treatment experience. This "static AI" architecture limits their value in complex real-world clinical settings.

To address this bottleneck, a team led by Dr. Lian Zhang from the First Hospital of Hebei Medical University, in collaboration with domestic and international research partners, has proposed VIBEMed, which is a self-evolving multi-agent framework for clinical decision support designed to enable dynamic learning and safe, traceable system evolution.

Within the neurovascular unit, neurons, glial cells, brain microvascular endothelial cells, and pericytes all harbor mitochondria. When these organelles become dysfunctional, they drive and amplify neuroinflammation in various ways. A new review in Science Bulletin provides a systematic framework for summarizing nanomedicines that can precisely target damaged mitochondria, revealing a promising strategy for the treatment of central nervous system diseases.

Researchers have developed a facile water-based synthetic route for constructing fused azole–pyrimidine energetic materials, producing compounds with high thermal stability, strong detonation performance, and low mechanical sensitivity. The study demonstrates how eco-friendly aqueous synthesis can simultaneously improve safety, thermal resistance, and energetic performance in next-generation heat-resistant explosives.

This study demonstrates the gram-scale synthesis of water-stable M-Gallate (M = Mg, Ni, Co) metal-organic frameworks from inexpensive raw materials, and their application in harvesting water vapour from ultra-low humidity air. The optimal Mg-Gallate MOF achieves a record-high atmospheric water capture capacity of 170.0 mg/g at 0.2% relative humidity (RH) and 25 °C, surpassing all previously reported porous adsorbents under equivalent conditions.

Researchers have developed tough, conductive RBA hydrogels using a hyperbranched multi-arm crosslinking strategy. The hydrogels form a dense, stable network that enables wearable sensors to monitor full-body motion and support Morse-code-based human–machine interaction.