This paper outlines the development of artificial intelligence （AI） and its applications in traditional Chinese medicine （TCM） research， and elucidates the roles and advantages of large language models， knowledge graphs， and natural language processing in advancing syndrome identification， prescription generation， and mechanism exploration. Using spleen-stomach diseases as an example， it demonstrates the empowering effects of AI in classical literature mining， precise clinical syndrome differentiation， efficacy and safety prediction， and intelligent education， highlighting an upgraded research paradigm that evolves from data-driven and knowledge-driven approaches to intelligence-driven models. To address challenges related to privacy protection and regulatory compliance in cross-institutional data collaboration， a "trusted federated evidence-based analysis platform for TCM spleen-stomach diseases" is proposed， integrating blockchain-based smart contracts， federated learning， and secure multi-party computation. The deep integration of AI with privacy-preserving computing is reshaping research and clinical practice in TCM spleen-stomach diseases， providing feasible pathways and a technical framework for building a high-quality， trustworthy TCM big-data ecosystem and achieving precision syndrome differentiation.

To investigate the risk factors for acute kidney injury (AKI) following the use of amphotericin B deoxycholate and to develop a predictive model to guide clinical monitoring and intervention.

ObjectiveBased on 16S rDNA technology and molecular biology methods， the molecular mechanism of Shenling Baizhusan in the treatment of diarrhea-predominant irritable bowel syndrome （IBS-D） was investigated. MethodsThe 42 SD rats with SPF were randomly divided into no control group， SLBZS-H， medium （SLBZS-M）， low （SLBZS-L） dose group， positive control group and model group， with 7 rats in each group. The rat model of IBS-D was prepared by ice-cold senna （0.45 g∙mL^-1） gavage （10 mL∙kg^-1） combined with restraint stress for 14 consecutive days. After successful modeling， the corresponding drugs were given to each group with a gavage volume of 10 mL∙kg^-1： The positive group was administered with 2.36 ， 1.18， 0.59 g∙mL^-1 of Shenling Baizhusan in the Positive group and the Model group with the same volume of normal saline for 14 d. The general condition of the rats： Weight， feces， mental state and death were observed and recorded. The body weight， abdominal wall retraction reflex score （AWR） and loose stool rate of rats in each group were measured before （the first day）， after the model （day 14） and after treatment （day 28）. Hematoxylin-eosin staining was used to observe the morphological characteristics of colon tissues of experimental animals. Enzyme-linked immunosorbent assay was used to quantitatively analyze the concentration of inflammatory mediators in the peripheral blood of experimental animals. Western blotting was used to detect the expression levels of key proteins of Toll-like receptor 4 （TLR4）， Toll-like receptor 2 （TLR2）， myeloid differentiation factor 88 （MyD88） and nuclear factor-κB （NF-κB） signaling pathway in rat colon tissue. 16S rDNA technology was used to detect the structural changes of intestinal microbiota in rats. ResultsCompared with Control， the colon of the Model group showed partial mucosal epithelial shedding and inflammatory cell infiltration. The contents of TNF-α， IL-1β， IL-6 and 5-HT in serum increased （P<0.05）， the protein expressions of TLR2， TLR4， MyD88 and NF-κB in colon tissue increased （P<0.05）， the diversity indices of Richness， Chao1， abundance-based coverage estimator（ACE） and Shannon decreased （P<0.05）， and the phylum Firmicutes， Actinobacteria， The relative richness of Bacteroides-H， Lactobacillus and Ligilactobacillus decreased （P<0.05）， while the relative richness of Bacteroidetes， Proteobacteria and Prevotella increased （P<0.05）. Compared with the model group， the colonic structure and organization of the SLBZS-H group， SLBZS-M group， SLBZS-L group and Positive group were clearer， and only a small number of inflammatory cells were present in some areas， and the serum contents of TNF-α， IL-1β， IL-6 and 5-HT were decreased （P<0.05）， TLR2， TLR4， The protein expressions of MyD88 and NF-κB decreased （P<0.05）， and compared with the model group， the diversity indices of Richness， Chao1， ACE and Shannon in the SLBZS-H， SLBZS-M and SLBZS-L groups increased （P<0.05）， and the richness of Firmicutes and Actinobacteria increased （P<0.05）. The richness of Proteobacteria and Prevotella decreased （P<0.05）， and the abundance of Prevotella decreased （P<0.05）， Bacteroides-H， Muribaculum， Lactobacillus and salivarius in the Positive group salivarius （P<0.05）. ConclusionShenling Baizhusan can effectively treat IBS-D， and its molecular mechanism may be to play a therapeutic role by improving intestinal flora and inhibiting the TLRS/MyD88/NF-κB signaling pathway to reduce inflammatory response.

Objective: To systematically characterize the developmental trajectory and interdisciplinary integration of intelligent diagnosis in traditional Chinese medicine (TCM) through quantitative topic evolution analysis, we addressed the fragmentation of existing research and clarified the long-term research structure and evolutionary patterns of the field. Methods: A topic evolution analysis was performed on Chinese-language literature pertaining to intelligent diagnosis in TCM. Publications were retrieved from the China National Knowledge Infrastructure (CNKI), Wanfang Data, and China Science and Technology Journal Database (VIP), covering the period from database inception to July 3, 2025. A hybrid segmentation approach, based on cumulative publication growth trends and inflection point detection, was applied to divide the research timeline into distinct stages. Subsequently, the latent Dirichlet allocation (LDA) model was used to extract research topics, followed by alignment and evolutionary analysis of topics across different stages. Results: A total of 3 919 publications published between 2003 and 2025 were included, and the research trajectory was divided into five stages based on data-driven breakpoint detection. The field exhibited a clear evolutionary shift from early rule-based systems and tongue-pulse image and signal analysis (2006 – 2010), to machine-learning-based syndrome and prescription modeling (2011 – 2015), followed by deep-learning-driven pattern recognition and formula association (2016 – 2020). Since 2021, research has increasingly emphasized knowledge-graph construction, multimodal integration, and intelligent clinical decision-support systems, with recent studies (2024 – 2025) showing the emergence of large language models and agent-based diagnostic frameworks. Topic evolution analysis further revealed sustained cross-stage continuity in syndrome modeling and prescription association analysis, alongside the progressive consolidation of integrated intelligent diagnostic platforms. Conclusion By identifying key technological transitions and persistent core research themes, our findings offer a structured reference framework for the design of intelligent diagnostic systems, the construction of knowledge-driven clinical decision-support tools, and the alignment of AI models with TCM diagnostic logic. Importantly, the stage-based evolutionary insights derived from this analysis can inform future methodological choices, improve model interpretability and clinical applicability, and support the translation of intelligent TCM diagnosis from experimental research to real-world clinical practice.

Chronic post-surgical pain (CPSP) is a common long-term complication following lung surgery. Its high incidence significantly impacts patients’ quality of life and functional recovery, and imposes a substantial socioeconomic burden. This consensus aims to systematically establish a standardized integrated Chinese and Western medicine diagnostic and treatment framework for chronic post-lung surgery pain (CPLSP). Based on the latest domestic and international evidence-based medical research and multidisciplinary clinical experience, the working group comprehensively elaborates on core issues regarding CPLSP, including its definition, epidemiology, pathogenesis, clinical assessment, Western medical treatment, traditional Chinese medicine (TCM) treatment, and integrated strategies. The consensus emphasizes a patient-centered approach, adhering to the principles of multimodality, individualization, and stepwise management, highlighting the synergistic advantages of integrating Chinese and Western medicine throughout the entire perioperative management cycle encompassing "perioperative anti-inflammation, acute analgesia, and chronic rehabilitation." Through systematic literature retrieval and evidence integration, a total of 9 core recommendations were established to provide scientifically sound and clinically practical guidance.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

The surgical management of empyema (excluding those caused by mycobacterium tuberculosis and non-tuberculous mycobacteria) is rapidly evolving towards minimally invasive, precise, and stepwise approaches. The traditional three-stage classification (exudative, fibrinopurulent, and organizing) has limitations in guiding dynamic clinical decision-making. For the first time, this consensus explicitly identifies two critical junctures in the pathological progression of empyema: "early transformation" (stage Ⅰ to Ⅱ) and "late transformation" (stage Ⅱ to Ⅲ), and thereby constructs a corresponding "identification-early warning-intervention" stepwise therapeutic framework. The consensus emphasizes that proactive debridement via video-assisted thoracoscopic surgery should be performed during the early transformation phase to halt disease progression. Conversely, during the late transformation phase, therapeutic goals should be rationally adjusted to prioritize adequate drainage, avoiding futile pleural decortication. Moreover, the consensus underscores the pivotal role of precise perioperative etiological diagnosis (e.g. metagenomic nest-generation sequencing) and standardized anti-infective therapy. Integrating practical experiences from multiple thoracic surgery centers in China and relevant evidence-based literature, this consensus formulates recommendations on the precise definitions of staging, surgical indications for each phase, key technical points, perioperative management, and training systems. It aims to promote the standardized and individualized surgical management of empyema, ultimately optimizing patient prognosis.

BACKGROUND: Based on the China-VHD database, this study sought to develop and validate a Valvular Heart Disease- specific Age-adjusted Comorbidity Index (VHD-ACI) for predicting mortality risk in patients with VHD. METHODS & RESULTS: The China-VHD study was a nationwide, multi-centre multi-centre cohort study enrolling 13,917 patients with moderate or severe VHD across 46 medical centres in China between April-June 2018. After excluding cases with missing key variables, 11,459 patients were retained for final analysis. The primary endpoint was 2-year all-cause mortality, with 941 deaths (10.0%) observed during follow-up. The VHD-ACI was derived after identifying 13 independent mortality predictors: cardiomyopathy, myocardial infarction, chronic obstructive pulmonary disease, pulmonary artery hypertension, low body weight, anaemia, hypoalbuminaemia, renal insufficiency, moderate/severe hepatic dysfunction, heart failure, cancer, NYHA functional class and age. The index exhibited good discrimination (AUC, 0.79) and calibration (Brier score, 0.062) in the total cohort, outperforming both EuroSCORE II and ACCI (P < 0.001 for comparison). Internal validation through 100 bootstrap iterations yielded a C statistic of 0.694 (95% CI: 0.665-0.723) for 2-year mortality prediction. VHD-ACI scores, as a continuous variable (VHD-ACI score: adjusted HR (95% CI): 1.263 (1.245-1.282), P < 0.001) or categorized using thresholds determined by the Yoden index (VHD-ACI ≥ 9 vs. < 9, adjusted HR (95% CI): 6.216 (5.378-7.184), P < 0.001), were independently associated with mortality. The prognostic performance remained consistent across all VHD subtypes (aortic stenosis, aortic regurgitation, mitral stenosis, mitral regurgitation, tricuspid valve disease, mixed aortic/mitral valve disease and multiple VHD), and clinical subgroups stratified by therapeutic strategy, LVEF status (preserved vs. reduced), disease severity and etiology. CONCLUSION The VHD-ACI is a simple 13-comorbidity algorithm for the prediction of mortality in VHD patients and providing a simple and rapid tool for risk stratification.

[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

Loss-of-function variants in CSDE1 have been strongly linked to neuropsychiatric disorders, yet the precise role of CSDE1 in neurogenesis remains elusive. In this study, we demonstrate that knockout of Csde1 during cortical development in mice results in impaired neural progenitor proliferation, leading to abnormal cortical lamination and embryonic lethality. Transcriptomic analysis revealed that Csde1 upregulates the transcription of genes involved in the cell cycle network. Applying a dual thymidine-labelling approach, we further revealed prolonged cell cycle durations of neuronal progenitors in Csde1-knockout mice, with a notable extension of the G1 phase. Intersection with CLIP-seq data demonstrated that Csde1 binds to the 3' untranslated region (UTR) of mRNA transcripts encoding cell cycle genes. Particularly, we uncovered that Csde1 directly binds to the 3' UTR of mRNA transcripts encoding Cdk6, a pivotal gene in regulating the transition from the G1 to S phases of the cell cycle, thereby maintaining its stability. Collectively, this study elucidates Csde1 as a novel regulator of Cdk6, sheds new light on its critical roles in orchestrating brain development, and underscores how mutations in Csde1 may contribute to the pathogenesis of neuropsychiatric disorders.
Animals ; Neurogenesis/genetics* ; Cell Cycle/genetics* ; Mice, Knockout ; Mice ; Neural Stem Cells/metabolism* ; DNA-Binding Proteins/metabolism* ; Cyclin-Dependent Kinase 6/genetics* ; Cell Proliferation ; 3' Untranslated Regions ; Cerebral Cortex/embryology* ; RNA-Binding Proteins ; Mice, Inbred C57BL