Research across the full arc of medicine.

Calvarial Bone Channels and a Putative Craniomaxillary–Brain Interface in Central Nervous System Immunity

Ziwen Sheng, Yulong Zhang, Hao Wang, et al.

Decoding the Gut‐Brain Axis: Multi‐Omics Reveals a Causal Chain Linking Human Genome, Gut Microbiota, Blood Metabolites, and Alertness in the Brain

Hongru Li, Chuansheng Chen, Ofir Turel, et al.

This multi‐omics study identified genetic loci, gut microbiota, blood metabolites, and gray matter volumes of orbitofrontal cortex and cerebellum that were potentially relevant to alerting in healthy Chinese adults. Subsequent two‐step one‐sample Mendelian randomization further revealed a serial mediation pathway: Bacteroides intestinalis influences the GMV of OFC and cerebellum through N‐pentadecanoylsphingosine‐1‐phosphocholine and alerting.

Advancing Artificial Intelligence in Myopia Management: From Predicting Progression to Supporting Clinical Decisions

Zewei Zhang, Yun Wang, Lifei Zhang, et al.

The AI‐enabled multimodal framework for whole‐life myopia management. Graphical

Chronic Respiratory Diseases in the 2023‐Defined WHO Western Pacific Region: Burden and Trends From GBD 2023

Zhang Yue, Yi‐Cheng Fu, Wen Zuo, et al.

Chronic respiratory disease burden in the 2023‐defined WHO Western Pacific Region, 1990–2023: Using the Global Burden of Disease 2023 estimates, this study characterized the incidence, prevalence, deaths, and disability‐adjusted life years (DALYs) associated with chronic respiratory diseases (CRDs) in the 37 WHO Western Pacific Region Member States and areas as defined on 31 December 2023 (WPR‐37). Between 1990 and 2023, absolute numbers of incident cases, prevalent cases, deaths, and DALYs increased, whereas age‐standardized mortality and DALY rates declined substantially, indicating that population growth and aging expanded healthcare demand despite improvements in age‐standardized fatal and disabling outcomes. The remaining burden was concentrated among older adults and males and was dominated by chronic obstructive pulmonary disease. Tobacco smoking and air pollution were the leading selected modifiable contributors, followed by occupational exposure. Considerable heterogeneity was observed among selected major countries, supporting a policy pathway integrating tobacco control, air‐quality improvement, occupational protection, earlier detection, long‐term respiratory disease m…

Organoid Coculture Models for Cancer Research and Immunotherapy

Zhuo Yang, Huihui Yang, Guangxin Li, et al.

This review summarizes organoid coculture platforms, including submerged Matrigel culture, air–liquid interface, microfluidic/organoid‐on‐a‐chip, and 3D bioprinting, for reconstructing the tumor microenvironment to study tumor–stroma/immune crosstalk, evaluate immunotherapies, and enable drug screening. Phenotypic readouts range from histology and multiomics to spatial transcriptomics and AI‐driven 3D imaging. We further discuss clinical applications, key challenges, and future directions, with an emphasis on translating these platforms toward clinically actionable personalized immunotherapy.

Recent Advances in Exosome‐Based Nanodelivery Systems for Traumatic Brain Injury Treatment

Jue Zhu, Weiquan Liao, Ziqi Lian, et al.

An overview of functional modification, therapeutic effects, molecular composition, and delivery strategies for exosomes.

Systematic Investigation of Tumor Microenvironment and Antitumor Immunity With IOBR

Yiran Fang, Yuyun Kong, Guangda Rong, et al.

The tumor microenvironment (TME) is a critical factor in antitumor immunity and treatment outcome in cancer therapy. We have developed an analysis tool called the immuno‐oncology biological research (IOBR) to investigate the TME and its role in antitumor immunity. Leveraging multi‐omics data, IOBR facilitates comprehensive analysis of TME characteristics, immune interactions, and their impact on immunotherapy outcomes. IOBR features six modules for TME analysis, including transcriptomic data preprocessing, TME profiling, TME pattern identification, ligand–receptor interaction analysis, genome‐TME interaction assessment, and visualization, along with modeling. Since its release, this tool has been widely applied in many studies. In the future, IOBR will gradually integrate TCR/BCR repertoire analysis, enhance genomic functionalities, and develop spatial transcriptomics modules, which will further our understanding of TME dynamics and tumor immunity.

Optimized Dynamic Network Biomarker Deciphers a High‐Resolution Heterogeneity Within Thyroid Cancer Molecular Subtypes

Haonan Zhang, Ge Zhang, Pengyuan Xu, et al.

The progression of differentiated thyroid carcinoma (DTC) poses significant clinical challenges, especially in determining the optimal time for intervention. To capture early signals of disease progression, we employed an optimized dynamic network biomarker (DNB) method—a systems biology approach that detects abrupt molecular changes indicating a critical transition signal. This analysis revealed that Stage II marks a critical transition in the disease trajectory. We further developed a scoring system called TCPSLevel (Thyroid Carcinoma Progression Signature Level), which quantifies individual progression risk based on gene expression profiles. TCPSLevel showed strong associations with clinical features and prognosis across multiple datasets. Our ensemble consensus clustering approach uncovered three robust DTC molecular subtypes, which demonstrated distinct clinical outcomes, immune microenvironments, regulatory landscapes, and therapeutic agents. A clinically applicable classifier (miniPC) was constructed using machine learning to facilitate subtype prediction. We also identified ASPH as a key regulator driving progression and validated its expression and function experimentally. Together, these findings offer new insights and practical tools for early risk assessment and personalized management of thyroid cancer.

GseaVis: An R Package for Enhanced Visualization of Gene Set Enrichment Analysis in Biomedicine

Jun Zhang, Hongyuan Li, Wenjun Tao, et al.

Gene set enrichment analysis (GSEA) is a widely used computational method for determining whether predefined sets of genes show statistically significant concordant differences between two biological states. Despite its popularity, effective visualization of GSEA results remains challenging particularly for users seeking to extract meaningful insights without extensive programming knowledge. Although several tools are available for visualizing GSEA results, many lack the flexibility and customization options necessary for a comprehensive exploration of the data. For instance, the desktop GSEA software generates basic plots that are not publication ready and offer limited options for editing or modification. Users often encounter difficulties adjusting graphical parameters to achieve the desired level of customization or visual quality. Furthermore, traditional tools often fail to meet the demands of emerging analytical needs. For instance, they will lack the capability to effectively compare pathway activity levels across multiple experimental conditions. To bridge this gap, we introduce GseaVis, a user‐friendly R package specifically designed to simplify and enhance the visualization of GSEA results. GseaVis provides a variety of highly customizable and publication‐ready plots including enrichment plots, ranked gene heatmaps, and other forms of graphic visualizations of enriched gene sets. With its simple interface and flexibility, our tool significantly lowers the barrier for biologists and bioinformaticians to explore and present their GSEA data clearly and effectively. The GseaVis package is available on GitHub and is integrated with well‐established R libraries, allowing easy data manipulation and seamless integration into existing bioinformatics workflows. The GseaVis is publicly available via GitHub ( https://github.com/junjunlab/GseaVis ) for users’ access. A complete description of the usages can be found on the manuscript’s GitHub page ( https://junjunlab.github.io/gseavis‐manual/ ).

Breaking Boundaries: Chronic Diseases and the Frontiers of Immune Microenvironments

Guoqing Li, Yuxuan Wan, Alan Jiao, et al.

The immune microenvironment includes immune cells, cytokines, extracellular matrix, vesicles, etc. The interactions between these components form a unique local immune microecology. Although immunity serves as the defense against external pathogens, aberrant immune activation often contributes to disease development. Chronic diseases, a broad category of noncommunicable conditions characterized by long latency and prolonged course, are increasingly recognized for their intricate relationship with the immune microenvironment. Herein, we comprehensively summarize how the immune microenvironment, through its complex regulatory network, influences the progression and manifestation of chronic diseases. We further explore the potential of targeting the immune microenvironment as a therapeutic strategy, aiming to provide new insights and directions for the prevention, diagnosis, and treatment of chronic diseases.

Heterogeneity of Intratumoral Microbiota Within the Tumor Microenvironment and Relationship to Tumor Development

Zhifei Gao, Aimin Jiang, Zizhuo Li, et al.

Intratumoral microorganisms within solid tumor TMEs significantly influence tumorigenesis and development by altering immune and metabolic patterns. Their diverse compositions and species contribute to the structural and functional heterogeneity of the TME, affecting tumor progression. Understanding the dual roles of these microbes in antitumor and protumor activities and their complex interactions with the TME enhances our knowledge of the mechanisms underlying tumorigenesis and development.

Cloud‐Based GWAS Platform: An Innovative Solution for Efficient Acquisition and Analysis of Genomic Data

Xiaohong Ke, Kailai Li, Aimin Jiang, et al.

Genome‐wide association studies (GWAS) have identified over 50,000 disease‐associated genetic variants, yet traditional data acquisition and analysis workflows face critical limitations, including inefficient terabyte‐scale data downloading, prohibitive computational infrastructure requirements, and complex cross‐database integration challenges, that impede research accessibility and clinical translation. We developed a cloud‐based GWAS platform integrating data from major international databases (GWAS Catalog, UK Biobank, and FinnGen) encompassing 40,000+ phenotypes across neuroimaging, proteomics, microbiome, metabolomics, and immunology. The platform employs a Kubernetes‐based distributed architecture with hybrid storage systems and optimized indexing structures. We complemented this with FastGWASR, an R package providing seamless integration for Mendelian randomization, drug target validation, and multiomics analyses. Performance evaluations demonstrated second‐level data extraction capabilities with > 99% reduction in local storage requirements and significant hardware demand reduction compared to traditional methods. The platform successfully processed large‐scale analyses, including MR‐PheWAS studies and multiomics integration workflows. Case studies validated platform effectiveness in metabolite‐diabetes causal analysis, PCSK9 drug target validation, and gut microbiome‐inflammation network analysis, achieving comparable scientific accuracy with dramatically improved efficiency. This cloud‐based ecosystem addresses fundamental barriers in GWAS research by democratizing access to genomic data analysis capabilities. The platform's integration of comprehensive data resources with user‐friendly analytical tools accelerates genomic discovery translation into precision medicine applications, particularly benefiting resource‐limited institutions and facilitating collaborative research across disciplines.