ObjectiveTo address the challenges of unstructured classical Chinese expressions， nested entity relationships， and limited annotated data in famous traditional Chinese medicine（TCM） case records， this study proposes a joint relation extraction framework that integrates data augmentation and entity mapping， aiming to support the construction of TCM diagnostic knowledge graphs and clinical pattern mining. MethodsWe developed an annotation structure for entities and their relationships in TCM case texts and applied a data augmentation strategy by incorporating multiple ancient texts to expand the relation extraction dataset. A cascade binary tagging framework for relation triple extraction（CasRel） model for TCM semantics was designed， integrating a pre-trained bidirectional encoder representations from transformers（BERT） layer for classical TCM texts to enhance semantic representation， and using a head entity-relation-tail entity mapping mechanism to address entity nesting and relation overlapping issues. ResultsExperimental results showed that the CasRel model， combining data augmentation and entity mapping， outperformed the pipeline-based Bert-Radical-Lexicon（BRL）-bidirectional long short-term memory（BiLSTM）-Attention model. The overall precision， recall， and F₁-score across 12 relation types reached 65.73%， 64.03%， and 64.87%， which represent improvements of 14.26%， 7.98%， and 11.21% compared to the BRL-BiLSTM-Attention model， respectively. Notably， the F₁-score for tongue syndrome relations increased by 22.68%（69.32%）， and the prescription-syndrome relations performed the best with the F₁-score of 70.10%. ConclusionThe proposed framework significantly improves the semantic representation and complex dependencies in TCM texts， offering a reusable technical framework for structured mining of TCM case records. The constructed knowledge graph can support clinical syndrome differentiation， prescription optimization， and drug compatibility， providing a methodological reference for TCM artificial intelligence research.

This paper summarizes Professor LU Fang's clinical experience in treating systemic lupus erythematosus （SLE） based on the differentiation and treatment of heat-toxin and blood-stasis in the collaterals. SLE is generally characterized by deficiency in origin with excess in manifestation. The core pathogenesis is heat-toxin obstructing the collaterals. During the acute active stage， the predominant pattern is blazing heat-toxin causing blood stasis， while in the chronic remitting stage， the main pattern is toxic stasis blocking the collaterals with qi and yin deficiency. Clinical treatment follows the basic principle that treat with salty-cold herbs， when heat invades internally and that assist with acrid-dispersing herbs when stasis obstructs the collaterals. The self-formulated Yimian Decoction （抑免汤） serves as the base formula and is applied in stages. During the acute active stage， it is often combined with herbs for clearing heat and detoxifying， cooling blood and resolving stasis， and unblocking the collaterals. In the chronic remitting stage， it is often combined with herbs for activating blood circulation and unblocking the collaterals， as well as tonifying qi and nourishing yin.

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.

Objective To evaluate the predictive value of different machine learning models constructed in a multicenter environment for potential organ donors and verify their clinical application feasibility. Methods The study included 2 000 inpatients admitted to five domestic tertiary hospitals from January 2020 to December 2023, who met the criteria for potential organ donation assessment. They were randomly divided into a training set and an internal validation set (7∶3). Another 300 similar patients admitted to the First Affiliated Hospital of Harbin Medical University from January 2024 to April 2025 were included as an external validation set. The area under the curve (AUC), sensitivity, specificity, accuracy and F1-score of three models were compared, and the consistency of the potential organ donor determination process was tested. Multivariate logistic regression analysis was used to identify predictive factors of potential organ donors. Decision curve analysis (DCA) was employed to verify the resource efficiency of each model, and the threshold interval and intervention balance point were assessed. Results Apart from age, there were no significant differences in other basic characteristics among the centers (all P>0.05). The consistency of the potential organ donor determination process among researchers in each center was good [all 95% confidence interval (CI) lower limits >0]. In the internal validation set, the XGBoost model had the best predictive performance (AUC=0.92, 95% CI 0.89-0.94) and the best calibration (P=0.441, Brier score 0.099). In the external validation set, the XGBoost model also had the best predictive performance (AUC=0.91, 95% CI 0.88-0.94), outperforming logistic regression and random forest models. Multivariate logistic regression showed that mechanical ventilation had the greatest impact (odds ratio=2.06, 95% CI 1.54-2.76, P<0.001). DCA indicated that the XGBoost model had the highest net benefit in the threshold interval of 0.2-0.6. The “treat all” strategy only had a slight advantage at extremely low thresholds. The recommended threshold interval, which balances intervention costs and clinical benefits, considers ≥50% positive predictive value (PPV) and ≤50 referrals per 100 high-risk patients. Conclusions The XGBoost model established in a multicenter environment is accurate and well-calibrated in predicting potential organ donors. Combined with DCA, it may effectively guide the timing of clinical interventions and resource allocation, providing new ideas for the assessment and management of organ donation after brain death.

Compared with traditional therapies for chronic liver disease （CLD）， Bifidobacterium has the characteristics of multi-target intervention， high biosafety， and good host compatibility and provides new strategies for intervention of CLD progression in terms of microecological regulation. Various studies have shown that Bifidobacterium regulates liver homeostasis and exerts a therapeutic effect on CLD by regulating intestinal flora， maintaining antioxidation， promoting energy consumption， alleviating inflammation， improving glycolipid metabolism， and exerting an antitumor effect. This article systematically reviews the studies on Bifidobacterium in the treatment of CLD in China and globally， explores their different mechanisms， and elaborates on the interaction between related signaling pathways （such as the nuclear factor erythroid 2-related factor 2 signaling pathway and the adenosine monophosphate-activated protein kinase signaling pathway） and the liver， in order to provide a basis for probiotic intervention in liver pathology， as well as new ideas for the comprehensive treatment of CLD.

ObjectiveTo explore the complex network relationships among pain, kinesiophobia, social participation and knee function in patients after total knee arthroplasty (TKA), and to analyze the moderating effects of different socio-structural factors on the rehabilitation network from an ethical equity perspective. MethodsA convenience sampling method was used to select 291 patients who underwent TKA in Qilu Hospital of Shandong University from May to July, 2023. Pain was assessed using Numerical Rating Scale, kinesiophobia with Chinese short version of the Tampa Scale for Kinesiophobia, social participation with Impact on Participation and Autonomy Questionnaire, and knee function with Hospital for Special Surgery Knee Score. A partial correlation network among pain, kinesiophobia, social participation and knee function was constructed using Graphical Least Absolute Shrinkage and Selection Operator. Key variables were identified through node centrality and bridge centrality analysis. Network Comparison Tests (NCT) were used to analyze network differences among subgroups based on different socio-structural characteristics. ResultsIn the network model, the nodes with the highest strength centrality were indoor participation, activity behavior and activity pain. Bridge centrality analysis indicated that activity pain, knee function, indoor participation and activity cognition were key bridge nodes. NCT revealed no significant differences in overall network structure or global strength among subgroups based on residence, education level or payment method (P > 0.05). However, significant differences in edge weights were found for specific edges such as activity cognition-activity behavior and knee function-indoor participation (P < 0.05). ConclusionThere is a network of interactions among pain, kinesiophobia, social participation and knee function in patients after TKA, with nodes such as indoor participation and activity pain playing key roles in the rehabilitation process. Although the overall rehabilitation network is similar across different socio-structural groups, variations exist in specific relational pathways among patients from rural areas, those with lower education levels, and those with out-of-pocket payment. This suggests that clinical rehabilitation interventions should focus on these core nodes and implement targeted support strategies for socio-structurally disadvantaged groups to promote rehabilitation equity.

Disease computable phenotype is a data model designed to identify specific clinical conditions or characteristics, which automatically extracts information from clinical databases such as electronic health records through algorithms. Phenotypic data for rare diseases often reside in unstructured text. Due to the scarcity of rare disease cases, atypical symptoms, and insufficient physician experience, misdiagnosis and underdiagnosis rates remain high. In this context, the application of computable phenotype technology holds promise for improving the accuracy and efficiency of rare disease diagnosis. This article reviews the current research status, challenges, and opportunities of computable phenotype technology in biomedicine, particularly in the field of rare diseases, and proposes a development and validation framework for rare disease computable phenotypes, aiming to provide research and development insights for computable phenotypes to empower the diagnosis and treatment of rare diseases.

Objective To understand the epidemiological characteristics of newly reported occupational diseases and provide a basis for the formulation of occupational disease prevention and control plans in Nanjing. Methods A descriptive analysis was conducted on newly reported occupational disease cases in Nanjing from 2014 to 2024. Results A total of 325 new cases of occupational diseases were reported in Nanjing, primarily concentrated in occupational otorhinolaryngological and oral diseases, as well as pneumoconiosis. Male cases outnumbered female cases across all types of occupational diseases. The median age at diagnosis was 53 (44, 65) years, and the median length of employment was 13 (6, 24) years. The distribution of occupational diseases varied significantly by gender and age at diagnosis (P<0.01). The distribution of occupational diseases also showed significant differences based on the length of exposure to hazards (χ²=120.63, P<0.01). Large enterprises, state-owned enterprises, and manufacturing industries accounted for the majority of cases (120 cases, 36.92%; 154 cases, 47.38%; 232 cases, 71.38%). The distribution of newly reported occupational diseases across different age groups at diagnosis was statistically significant (H=97.66, P<0.01; H=84.06, P<0.01; H=34.64, P<0.01; H=20.05, P<0.01; H=21.70, P<0.01). Except for occupational diseases caused by physical factors, the distribution of other newly reported occupational diseases across different employment length groups was also statistically significant (H=105.45, P<0.01; H=97.05, P<0.01; H=34.14, P<0.01; H=42.69, P<0.01). Conclusion The prevention and control of newly reported occupational diseases in Nanjing remain challenging. Attention should be paid to key occupational otorhinolaryngological and oral diseases, as well as pneumoconiosis. It is necessary to strengthen supervision and management of medium and large state-owned enterprises and manufacturing industries.

Objective To investigate the association between physical activity levels and blood lipids among college freshmen, and to provide scientific evidence for the health management of college freshmen. Methods An electronic questionnaire survey on physical activity was conducted on freshmen of a university, and fasting blood biochemical indicators were detected. The International Physical Activity Questionnaire (IPAQ) short form was used to evaluate the physical activity levels of the participants. Dyslipidemia was defined as an abnormality in any one of the following serum lipid parameters: total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), or non-high-density lipoprotein cholesterol. Binary logistic regression and stratified analyses were employed to explore the relationship between physical activity and blood lipids. Results A total of 3 401 participants were included, with an average age of 18.45 ± 0.92 years, and 60.5% were female. The prevalence of dyslipidemia was 17.7%, with a higher rate among males (22.1%) than females (14.8%). After adjusting for confounding factors related to blood lipids, high-intensity physical activity was negatively associated with the risk of elevated LDL-C among males (OR = 0.36, 95% CI: 0.13–0.99, P = 0.049). Conclusion Among freshmen at a medical college in Hubei Province, high-intensity physical activity is negatively associated with the risk of elevated LDL-C in males, but this association needs to be further confirmed by larger prospective cohort studies.

ObjectiveThis study aims to investigate whether Xixintang could ameliorate cognitive dysfunction in an Alzheimer's disease （AD） rat model induced by D-galactose and β-amyloid （Aβ_25-35）， by means of repairing the colonic mucosal barrier， regulating the Toll-like receptor 4 （TLR4）/nuclear factor-κB p65 （NF-κB p65） signaling pathway， and intervening in the pathological process mediated by the gut-brain axis. MethodsSixty specific pathogen-free （SPF） male Sprague-Dawley （SD） rats were randomly divided to five groups （n=12）： A control group， a model group， a donepezil group， an Xixintang group， and a probiotic group. Except for those in the control group， rats in all other groups received daily intraperitoneal injections of D-galactose for six consecutive weeks. Subsequently， aggregated Aβ_25-35 was injected stereotactically into the bilateral ventricles to establish the AD model. During the intervention periods， the rats in all groups were administered their respective drugs and normal saline by gavage. The Morris water maze test was used to assess the capacity for spatial learning and memory. Hematoxylin-eosin （HE） staining was employed to observe the histopathological changes in the colon tissues. Immunofluorescence was used to detect Aβ_1-41 deposition in the hippocampal region and Mucin 2 （MUC2） expression in the colonic mucosa. Western blot was performed to measure the protein expression levels of FFAR2，TLR4， NF-κB p65， occludin （OCLN）， zonula occludens-1 （ZO-1）， and MUC2 in the colonic tissues. Enzyme-linked immunosorbent assay （ELISA） was used to determine the contents of interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， serum amyloid A （SAA）， and Aβ_1-42 in the hippocampal region from the colonic tissues. The lipopolysaccharide （LPS） concentrations in colon tissues of rats were measured by using a dynamic chromogenic limulus assay. ResultsCompared with those in the control group， the rats in the model group exhibited a significantly prolonged escape latency and a markedly shorter duration in the target quadrant （P<0.01）. The integrity of the colonic mucosal structure was compromised， with disordered gland arrangement and a reduced number of goblet cells. The Aβ_1-42 deposition in the hippocampal region was significantly increased （P<0.01）. The protein expression levels of TLR4 and NF-κB p65 in colonic tissues were significantly upregulated （P<0.01）， while those of occludin and ZO-1 were downregulated （P<0.01）. The contents of inflammatory factors such as IL-6， TNF-α， and SAA were significantly elevated （P<0.01）， and the LPS level in the serum was markedly increased （P<0.01）. In comparison to those in the model group， the rats in the Xixintang group showed a significantly shortened escape latency and a prolonged duration in the target quadrant （P<0.01）. The colonic mucosal structure was ameliorated， with neat gland arrangement and an increased number of goblet cells. The Aβ_1-42 deposition in the hippocampal region was reduced （P<0.01）. The protein expressions of TLR4 and NF-κB p65 in the colon tissues were decreased （P<0.05，P<0.01）， while the protein levels of occludin and ZO-1 were increased （P<0.01）. The contents of IL-6， TNF-α， and serum amyloid A （SAA） were decreased （P<0.01）， and the LPS level was reduced （P<0.01）. ConclusionXixintang can significantly ameliorate cognitive dysfunction of AD model rats， by means of restoring the colonic mucosal barrier structure， reducing cerebral Aβ deposition， and suppressing peripheral and central inflammatory response. Its mechanism of action may be closely associated with the suppression of the TLR4/NF-κB signaling pathway activation， reduction of endotoxin levels， and regulation of the gut-brain axis.