Quick Entry

  • Submit manuscript Submit manuscript
  • Office work Office work
  • Latest Articles

  • PERSPECTIVE

    Hui-Yao Huang, Le Wang, Sheng Xu, Shuo-Peng Jia, Dan-Dan Kong, Xue-Jing Zhang, Si-Qi Wang, Han-Qing He, Hao-Ran Chen, Lu-Zhu Xia, Lan-Wei Guo, Yu Tang, Ling-Bin Du, Ning Li

    Corrected Proof
    Vol. 12, (2025) DOI: 10.1186/s40779-026-00685-9
    Abstract：Target trial emulation (TTE) has demonstrated popularity because of its ability to improve the reliability of causal inference from observational data. Nevertheless, knowledge about the current use, potential challenges, and insights of target trials in oncology is scarce. A total of 90 TTE studies in cancer areas were identified through systematic reviews in PubMed and Embase. Among the 54 applications in cancer treatment, registry databases (44.4%) and overall survival (OS, 63.0%) were predominantly used as data sources and primary endpoints, respectively. Approximately 30 (55.6%) of the included TTE cases were associated with immortal time bias, and 21 (38.9%) were associated with prevalent user bias. Among the 21 trials from 13 studies that aimed to calibrate the results from preexisting randomized controlled trials (RCTs), only 42.9% met both statistical agreement and estimate agreement. The availability of fit-for-purpose data sources and uncertainty about result concordance were the main hurdles limiting the quantity and quality of TTE in oncology areas. Promoting regulatory acceptance by initiating special projects could be crucial for the expanded application of real-world data (RWD) using TTE. Potential solutions,such as the integration of electronic medical records at the regional or country level, linkage with insurance claims databases, the modernization of eligibility criteria, the use of OS as the primary endpoint, and other best practices,were recommended for improving the feasibility and quality of oncology TTE.  
    <HTML>
    <L-PDF><Meta-XML>
    <Citation> <Bulk Citation> 151841751 false

    9

    13

    0

  • RESEARCH

    Wei-Qiang Chen, Li Lou, Xiao-Ling Qiang, Cassie Shu Zhu, Jian-Hua Li, Shu-Jin Chen, Brian Xiong, Huan Yang, Ping Wang, Kevin J. Tracey, Hai-Chao Wang

    Corrected Proof
    Vol. 12, (2025) DOI: 10.1186/s40779-026-00686-8
    Abstract：BackgroundSepsis and rheumatoid arthritis (RA) are distinct yet mechanistically related conditions commonly driven by dysregulated inflammatory responses. Here, we explored the counterintuitive hypothesis that an epitope from a deleterious anti-tetranectin (TN) antibody (mAb9) could hold unforeseen therapeutic potential.MethodsBy mapping mAb9’s epitope to P2 (residues 55–70), a region crucial for TN’s protective functions, we developed P2-1, a water-soluble derivative as a targeted therapy. We then employed animal models of sepsis (cecal ligation and puncture) and arthritis (collagen antibody-induced arthritis) to evaluate the therapeutic effects of P2, P2-1, and a procathepsin L (pCTS-L)-neutralizing antibody by assessing septic survival, arthritis severity, pain sensi-tivity, and joint tissue histology. In parallel, we utilized a surface plasmon resonance (SPR) assay and computational modeling to examine the P2-1/high mobility group box 1 (HMGB1) interaction. Finally, we elucidate the effect of P2-1 on the HMGB1-induced release of pCTS-L and other cytokines and chemokines using primary human peripheral blood mononuclear cells (PBMCs).ResultsP2-1 significantly improved survival and reduced systemic inflammation in a sepsis model, and attenuated arthritis severity and pain sensitivity in an RA model, even with therapeutic administration after disease onset. Mecha-nistically, P2-1 exhibited high-affinity binding to HMGB1 and selectively suppressed HMGB1-induced cathepsin L (Ctsl) mRNA upregulation and pCTS-L secretion from human immune cells, crucially without perturbing other HMGB1-induced cytokines and chemokines. We further validated pCTS-L as a therapeutic target by demonstrating that a neu-tralizing antibody conferred potent antiarthritic effects, reducing joint inflammation, pain, and structural damage.ConclusionsOur findings introduce a paradigm-shifting drug discovery strategy that transforms insights from harm-ful antibody action into targeted therapeutics for the HMGB1-pCTS-L axis. This approach not only delivers P2-1 as a potent therapy but also establishes pCTS-L as a crucial mediator in inflammatory diseases such as sepsis and RA.  
    <HTML>
    <L-PDF><Meta-XML>
    <Citation> <Bulk Citation> 151841409 false

    5

    16

    0

  • PERSPECTIVE

    Jia Li, Zi-Chun Zhou, Zhen-Chang Wang, Han Lv

    Corrected Proof
    Vol. 12, (2025) DOI: 10.1186/s40779-026-00684-w
    Abstract：Artificial intelligence (AI) and big data are reshaping the healthcare landscape. However, clinical value depends on how well systems augment clinicians and fit into routine workflows. To this end, we introduce the TRIAD frame-work: trustworthy governance, real-world clinical value, and integrated adaptive deployment, to guide the develop-ment, validation, and deployment of clinical AI. TRIAD requires explicit data provenance and intended use, fairness auditing, and calibrated uncertainty. This framework evaluates the human-AI team in real workflows using teamlevel metrics, including accuracy, safety, workload, and patterns of acceptance, editing, and overriding. Deployment proceeds via staged rollouts with preregistered guardrails and continuous monitoring of performance and subgroup impact. TRIAD views intelligence as a property of the human-AI team rather than the AI model alone. Aligning gov-ernance, evaluation, and deployment around clinicians and patients enables durable gains in safety, equity, efficiency,and experience, thereby elevating clinical value.  
    <HTML>
    <L-PDF><Meta-XML>
    <Citation> <Bulk Citation> 151841386 false

    9

    8

    0

  • COMMENTARY

    Jie Wang, Zhao-Jie Lyu, Qi Zhang, William C. Cho, De-Chao Feng

    Corrected Proof
    Vol. 12, (2025) DOI: 10.1186/s40779-025-00683-3
    <HTML>
    <L-PDF><Meta-XML>
    <Citation> <Bulk Citation> 148533097 false

    26

    12

    0

  • REVIEW

    Jiang-Bo Fan, Qin-Yuan Li, Xi-Feng Feng, Si-Yuan Huang, Rui Wang, Feng-Ying Liao, Di Liu, Wen-Yi Liu, Jian-Hui Sun, Hua-Cai Zhang, Hui-Ting Zhou, Jian-Xin Jiang, Zhen Wang, Ling Zeng

    Corrected Proof
    Vol. 12, (2025) DOI: 10.1186/s40779-025-00678-0
    Abstract：The cytokine storm, a life-threatening systemic inflammatory syndrome, is the primary driver of multiorgan failure in different clinical situations, including severe infections, autoimmune diseases, chimeric antigen receptor (CAR)T cell immunotherapy for cancer, and genetic syndromes. This review focuses primarily on cytokine storms triggered by severe infections such as viral pneumonia and bacterial sepsis, and explores the underlying mechanisms of cytokine storms and potential therapeutic interventions. Cytokine storms are characterized primarily by the excessive release of proinflammatory cytokines, which are triggered by pathogen-associated molecular patterns (PAMPs),damage-associated molecular patterns (DAMPs), and PANoptosis, all of which activate immune signaling cascades. Amplification mechanisms involve positive feedback loops and the failure of negative feedback mechanisms, leading to uncontrolled inflammation. Like a pyrrhic victory, the excessive activation of the immune system eliminated invading pathogens but caused catastrophic damage due to multiple organ dysfunction syndrome (MODS), turning the life-saving response into a life-threatening war. Therapeutic strategies, including cytokine antagonists, Janus kinase (JAK) inhibitors, caspase inhibitors, glucocorticoids, and blood purification therapies, aim to interrupt the self-amplifying cycle of inflammation that propagates organ injury, thereby reducing MODS and mortality. Challenges include optimizing the treatment timing and patient stratification. Future research should focus on combination therapies and personalized medicine based on the heterogeneity of infections and sepsis. Advances in multiomics and targeted therapies provide new hope for managing infections and sepsis.  
    <HTML>
    <L-PDF><Meta-XML>
    <Citation> <Bulk Citation> 148533064 false

    15

    5

    0

  • RESEARCH

    Yu Wang, Yong-Bo Xiang, Xiao-Wei Chen, Tao Zhang, Jian-Yang Wang, Wen-Yang Liu, Lei Deng, Lu-Hua Wang, Shu-Geng Gao, Nan Bi

    Corrected Proof
    Vol. 12, (2025) DOI: 10.1186/s40779-025-00679-z
    Abstract：BackgroundDespite the predictive impact of circulating tumor DNA (ctDNA) minimal residual disease (MRD), accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lung cancer (NSCLC) patients to guide personalized therapy remains challenging. This study aimed to develop and validate an interpretable artificial intelligence-assisted model using global data resources.MethodsLiquid biopsy data, blood-based genomic alterations, clinicopathological features, and survival outcomes of stage I-III NSCLC patients who underwent surgery or definitive chemoradiotherapy were collected from 6 cohorts. PRIME (Progression Risk prediction by Interpretable Machine learning on ctDNA-MRD, Mutations, and clinical-therapeutic features) was trained by 6 machine learning algorithms across 4 cohorts and validated in 2 independent cohorts. Model performance was evaluated by the area under the curve (AUC) and interpreted by SHapley Additive exPlanations (SHAP). Whole-exome sequencing (WES) or whole-genome sequencing (WGS) of tumor tissue from 430 stage II-III NSCLC patients and RNA-sequencing (RNA-seq) data from 1149 subjects, sourced from The Cancer Genome Atlas, were used to validate the prognostic effect of mutations identified in peripheral blood and investigate the underlying mechanisms.ResultsA global dataset encompassing 781 blood samples from 493 patients was analyzed. Clinical stage, pre-treatment ctDNA, post-treatment MRD, blood-based Kelch-like ECH-associated protein 1 (KEAP1), serine/threonine kinase 11 (STK11), and cyclin-dependent kinase inhibitor 2A (CDKN2A) mutations, and treatment modality were significantly associated with the risk of disease progression and were thereby included in the model training. WES/WGS and RNA-seq confirmed the poor prognostic effect of KEAP1, STK11, and CDKN2A mutations, which were characterized by the suppressive tumor microenvironment and attenuated humoral immunity. The neural network (NN) model exhibited optimal prediction of treatment failure risk in the training (AUC = 0.85, 95% CI 0.81-0.89) and validation sets (AUC = 0.82, 95% CI 0.74-0.89). SHAP analysis indicated that MRD (+0.306), treatment modality (+0.128), and pre-treatment ctDNA (+0.043) ranked in the top 3 contributions. NN-PRIME outperformed single liquid biopsy biomarkers and clinical-therapeutic signatures, and demonstrated consistent robustness across different clinical scenarios. High-risk patients identified by NN-PRIME had poorer prognoses but derived significant benefits from adjuvant therapy after surgery.ConclusionsAs an interpretable model integrating readily-accessible and crucial clinical-genomic predictors, PRIME achieves enhanced performance, allowing for early outcome prediction, refined risk stratification, and personalized clinical decision-making.  
    <HTML>
    <L-PDF><Meta-XML>
    <Citation> <Bulk Citation> 148532860 false

    17

    22

    0

Archive

MORE
    2
    Issue2, 2026
    1
    Issue1, 2026
    12
    Issue12, 2025
    11
    Issue11, 2025
    10
    Issue10, 2025
    9
    Issue9, 2025
Loading...
0