Gut bacteria are known to be a key factor in many health-related concerns. However, the number and variety of them is vast, as are the ways in which they interact with the body’s chemistry and each other. For the first time, researchers from the University of Tokyo used a special kind of artificial intelligence called a Bayesian neural network to probe a dataset on gut bacteria in order to find relationships that current analytical tools could not reliably identify.

Water ecological health is crucial for sustainable ecosystems and human well-being. However, China's complex water environments present significant challenges for precise health assessments. After analyzing global water ecological practices, Wenqing Liu's team identified specific challenges to China's water ecological monitoring and assessment, including cognition, observation, and analysis gaps.

This study proposes an automatic liver segmentation method for computed tomography (CT) images based on an improved ResUNet, which integrates the advantages of UNet and ResNet architectures to achieve high-precision segmentation on 128×128pixel images. The model removes batch normalization layers and incorporates residual blocks, combined with data augmentation techniques, ultimately achieving excellent metrics such as a Dice coefficient of 93.08% and accuracy of 98.57%. Surpassing traditional methods, this approach provides efficient and reliable technical support for liver disease diagnosis.

This review article provides a comprehensive overview of stress granules (SGs) —membraneless organelles formed in response to cellular stress—and their interactions with other organelles. It explores their structure, function, roles in health and disease, especially neurodegeneration, and discusses methodologies used to study these interactions. SGs influence critical cellular pathways, and understanding their interplay with both membrane-bound and membraneless organelles can reveal potential therapeutic targets for diseases like ALS and FTD.

New research found that combining radiofrequency ablation (RFA) with anti-PD-1 immunotherapy provides dual therapeutic benefits for hepatocellular carcinoma (HCC), effectively controlling distant tumors while preventing recurrence. The combination therapy significantly enhances infiltration of CD8⁺ T cells and dendritic cells while reducing PMN-MDSCs, mediated through activation of CXCL10/JAK-STAT signaling pathways. Importantly, this approach establishes durable immune memory capable of inhibiting growth of rechallenge tumor.