At present， the systematic excavation of the clinical experience and academic thought of the Sichuan school of Chinese medicine vis-à-vis its dominant disease entities remains fragmentary， and replicable paradigms are scarce. Confronted with empirical fragmentation， data heterogeneity and decision-making subjectivity， the standardised distillation， inheritance and clinical translation of these distinctive experiences has become a critical bottleneck constraining the development of the Sichuan school. The integration of artificial-intelligence technologies in data processing， pattern recognition and intelligent decision-making has rendered deep mining of traditional Chinese medicine（TCM） clinical knowledge and patterns imperative. Constructing an intelligent modern TCM diagnostic-therapeutic-evaluative system is now the obligatory route for inheritance and innovation in Chinese medicine， and simultaneously provides a technological breakthrough for intelligent decision paradigms in the dominant diseases of the Sichuan school. Accordingly， this study adopts the regional academic school as its point of entry， focuses on the dominant diseases of the Sichuan school， and proposes an innovative pathway of "four-dimensional data-multi-modal fusion-multi-agent decision-making". Specifically， four data dimensions are defined and instantiated： （Ⅰ） knowledge from classical medical literature and historical case records. （Ⅱ） objective four-diagnosis phenotypic data. （Ⅲ） master physicians' prescribing regularities. （Ⅳ） characteristic mechanisms of renowned formulae. Leveraging multi-modal data fusion and generative artificial intelligence， the entire causal chain of Famous Physicians and Renowned Formulas is explicated to reconstruct the diagnostic-therapeutic cognitive logic of the regional school. Finally， a multi-agent collective-decision model is established and refined for the dominant diseases of the Sichuan school， capable of generating precise， individualised treatment regimens and thereby advancing an intelligent diagnostic-therapeutic paradigm that delivers more efficient and accurate clinical decision support.

ObjectiveThis paper aims to conduct the feature analysis and correlation analysis on the ocular collateral features and differential metabolites in patients with chronic hepatitis B （CHB） complicated by glucose metabolism disorder （GMD），particularly those with the damp-heat syndrome type，by integrating shadowless scleral imaging and metabolomics technologies. MethodsA total of 313 patients were recruited from the Hepatology and Endocrinology Outpatient Departments of Public Health Clinical Center of Chengdu according to the inclusion/exclusion criteria，and they were divided into a CHB group and a CHB complicated by GMD groups （damp-heat syndrome group and non-damp-heat syndrome group）. All patients underwent high-definition ocular image acquisition and feature extraction using an intelligent analysis system for shadowless scleral imaging to analyze the differences in the counting of morphological feature scores of ocular collaterals among groups. By using a digital sampling method，24 patients from each group were randomly selected，along with 20 healthy volunteers，for untargeted metabolomic analysis of peripheral serum. Differential metabolites were identified，statistically analyzed，and subjected to potential biomarker analysis and pathway enrichment. Spearman method was performed to conduct the correlation analysis on the differential ocular collateral features and differential metabolites，followed by correlation network construction. ResultsCompared with those in the CHB group，patients with CHB complicated by GMD showed significant changes in ocular collateral feature scores such as "hillock"，"blood vessels"，and "pale dusky coloration" （P<0.05）. In comparison with those in the healthy group，metabolites including N-acetylglucosamine，acetylhomoserine，and myo-inositol （AUC>0.7） were identified as potential biomarkers for the disease. Compared with those in the CHB complicated by GMD group with non-damp-heat syndrome，patients with damp-heat syndrome exhibited significant changes in feature scores of "plaques"，"yellow coloration"，"spleen"，and "gallbladder" （P<0.05）. In comparison with those in the healthy group，metabolites such as O²′-4a-cyclic tetrahydrobiopterin，theobromine，xanthurenic acid，and L-glutamic acid 5-phosphate （AUC>0.7） were identified as potential biomarkers for the damp-heat syndrome type. The Spearman correlation analysis reveals weak to moderate linear correlations between the differential scleral collateral features and metabolites. By constructing a "disease-syndrome" network of ocular diagnosis and metabolites，"xanthurenic acid-gallbladder" and "theobromine-plaque/yellow coloration" were identified as specific molecular-phenotypic correlated biomarker clusters for CHB complicated by GMD with dampness-heat syndrome. ConclusionPatients with CHB complicated by GMD demonstrate differential ocular diagnostic features and serum metabolites corresponding to disease states and dampness-heat syndrome. These objective biomarkers can guide both clinical syndrome differentiation and medication. The macro-micro integration based on ocular feature clusters and potential metabolic biomarkers offers an innovative approach to a combined traditional Chinese and Western medicine diagnosis and treatment model for this disease.

As traditional Chinese medicine （TCM） culture evolves， the academic thoughts of these practitioners， being a core component of TCM inheritance， are gradually shifting from traditional models to digital and intelligent approaches. However， this process faces challenges， including insufficient standardization of data collection and processing， low inheritance efficiency， and the risk of inheritance alienation. To address these issues， this paper proposed the construction of an intelligent platform following the "intelligence-transmission-modeling-linkage" path. "Intelligence" involves using smart perception technologies to accurately collect and classify diagnostic and therapeutic information from famous TCM practitioners， laying the foundation for digital inheritance； "transmission" focuses on leveraging artificial intelligence to mine and inherit the clinical experience of famous TCM practitioners， thereby establishing a "regional academic schools+group commonality" dynamic inheritance system； "modeling" integrates the academic thoughts and advantageous diseases of multiple schools to develop intelligent diagnostic and therapeutic models of famous TCM practitioners， resulting in personalized treatment plans； "linkage" involves constructing a clinical decision support system of famous TCM practitioners by integrating blockchain and generative intelligence， creating an AI digital avatar of TCM diagnostic and therapeutic knowledge. The "intelligence-transmission-modeling-linkage" intelligent inheritance model not only provides new ideas for the digital inheritance of TCM academic schools， but also offers strong support for the modernization and internationalization of TCM.

Objective:To investigate the efficacy of radiotherapy in patients with advanced esophageal cancer receiving first-line chemoimmunotherapy.Methods:A retrospective analysis was conducted on the data of 137 patients with Stage Ⅳ esophageal squamous cell carcinoma (ESCC) treated at our hospital from January 2018 to May 2023. These patients were divided into two groups: a group treated with first-line chemoimmunotherapy combined with radiotherapy (chemoimmunotherapy + radiotherapy group, n = 43) and a group treated with only chemoimmunotherapy ( n = 94). Inverse probability of treatment weighting (IPTW) was applied to balance baseline characteristics between the groups. With overall survival (OS) and progression-free survival (PFS) as study endpoints, the survival data were analyzed using the Kaplan-Meier method, the log-rank test, and the Cox regression method. Results:Before calibration, the chemoimmunotherapy + radiotherapy group significantly outperformed the sole chemoimmunotherapy group in median PFS (13.6 months vs. 7.0 months; HR: 0.501, 95% CI: 0.309-0.811, P = 0.005). After calibration using the COX proportional-hazards model for age, gender, Eastern Cooperative Oncology Group (ECOG) performance status, smoking history, T/N/M stage, and tumor location, the chemoimmunotherapy + radiotherapy group still had significant advantages in PFS (14.7 months vs. 7.0 months; HR: 0.441, 95% CI: 0.261-0.745, P = 0.002). IPTW analysis further confirmed this trend (13.9 months vs. 7.0 months; HR: 0.492, 95% CI: 0.304-0.795, P < 0.001). Specifically, the median OS of the chemoimmunotherapy + radiotherapy group demonstrated significant improvement in all analyses: pre-calibration (29.5 months vs. 18.0 months; HR: 0.507, 95% CI: 0.297-0.867, P = 0.013), after calibration using the Cox model (27.5 months vs. 16.7 months; HR: 0.470, 95% CI: 0.266-0.830, P = 0.009), and after calibration using IPTW (29.5 months vs. 16.9 months; HR: 0.448, 95% CI: 0.262-0.764, P < 0.001). Conclusions:The combination of radiotherapy and first-line chemoimmunotherapy can significantly improve survival outcomes of patients with advanced ESCC, suggesting its potential as a standard treatment strategy.

With the advancement of modern research methods,machine learning(ML)algorithms have been widely applied in traditional Chinese medicine(TCM)diagnosis,transforming subjective syndrome differentiation into a more objective process,thereby providing a feasible pathway for the objectification and quantification of TCM diagnostics.However,challenges persist,including the dual"black-box"nature of disease-syndrome models(lacking interpretability),missing spatiotemporal dynamic data,and the"disconnect"between clinical phenotyping and molecular biomarker research.Focusing on non-alcoholic fatty liver disease(NAFLD),this study proposes a novel framework guided by the biological"multiple-hit"theory and TCM's"disease-syndrome-symptom-stage"approach,which involves:Constructing a mathematical model of NAFLD progression(simple steatosis→steatohepatitis→fibrosis→cirrhosis)via multi-level(phenotypic-cellular-molecular)network modules;And developing an interpretable multidimensional model integrating syndrome-imaging phenomics(macro)and metabolomics-derived biomarkers(micro)to enable personalized NAFLD diagnosis.

Objective:To explore the current status of self-management behavior in patients with inflammatory bowel disease and to construct a structural equation model to explore the mediating role of health literacy between discharge readiness and self-management behavior.Methods:From March to September 2023, 310 hospitalized patients with inflammatory bowel disease in the Tenth People ′s Hospital Affiliated to Tongji University were selected as the research subjects by the convenience sampling method. A cross-sectional survey was conducted using the general information questionnaire, the Health Literacy Management Scale, the Discharge Readiness Scale, and the Self-management Behavior Scale. Results:Eventually, 303 valid questionnaires were retrieved. Among the 303 patients, there were 190 males and 113 females, with an average age of (35.63 ± 16.41) years old. The total score of health literacy of patients with inflammatory bowel disease was (94.35 ± 10.78) points, indicating a health literacy compliance rate of 74.3%(225/303). The total score of discharge readiness was (79.52 ± 9.16) points, and the total score of self-management behavior was (135.22 ± 17.36) points. In patients with inflammatory bowel disease, the discharge readiness was positively correlated with the total score of health literacy ( r=0.748, P<0.01), the discharge readiness was positively correlated with the total score of self-management behavior ( r=0.821, P< 0.01), and the health literacy was positively correlated with the total score of self-management behavior ( r=0.767, P<0.01). Health literacy may serve as a mediating variable, partially explaining the relationship between discharge readiness and self-management behavior in patients with inflammatory bowel disease. The mediating effect accounted for 47.5% of the total effect. Conclusions:Discharge readiness not only positively impacts self-management behavior among patients with inflammatory bowel disease but also indirectly influences this behavior through health literacy. It is essential for clinical healthcare professionals to prioritize the assessment of discharge readiness and health literacy among these patients. By improving discharge readiness and enhancing health literacy, clinical healthcare professionals can effectively support patients with inflammatory bowel disease in their self-management efforts.

Objective:To investigate the efficacy of radiotherapy in patients with advanced esophageal cancer receiving first-line chemoimmunotherapy.Methods:A retrospective analysis was conducted on the data of 137 patients with Stage Ⅳ esophageal squamous cell carcinoma (ESCC) treated at our hospital from January 2018 to May 2023. These patients were divided into two groups: a group treated with first-line chemoimmunotherapy combined with radiotherapy (chemoimmunotherapy + radiotherapy group, n = 43) and a group treated with only chemoimmunotherapy ( n = 94). Inverse probability of treatment weighting (IPTW) was applied to balance baseline characteristics between the groups. With overall survival (OS) and progression-free survival (PFS) as study endpoints, the survival data were analyzed using the Kaplan-Meier method, the log-rank test, and the Cox regression method. Results:Before calibration, the chemoimmunotherapy + radiotherapy group significantly outperformed the sole chemoimmunotherapy group in median PFS (13.6 months vs. 7.0 months; HR: 0.501, 95% CI: 0.309-0.811, P = 0.005). After calibration using the COX proportional-hazards model for age, gender, Eastern Cooperative Oncology Group (ECOG) performance status, smoking history, T/N/M stage, and tumor location, the chemoimmunotherapy + radiotherapy group still had significant advantages in PFS (14.7 months vs. 7.0 months; HR: 0.441, 95% CI: 0.261-0.745, P = 0.002). IPTW analysis further confirmed this trend (13.9 months vs. 7.0 months; HR: 0.492, 95% CI: 0.304-0.795, P < 0.001). Specifically, the median OS of the chemoimmunotherapy + radiotherapy group demonstrated significant improvement in all analyses: pre-calibration (29.5 months vs. 18.0 months; HR: 0.507, 95% CI: 0.297-0.867, P = 0.013), after calibration using the Cox model (27.5 months vs. 16.7 months; HR: 0.470, 95% CI: 0.266-0.830, P = 0.009), and after calibration using IPTW (29.5 months vs. 16.9 months; HR: 0.448, 95% CI: 0.262-0.764, P < 0.001). Conclusions:The combination of radiotherapy and first-line chemoimmunotherapy can significantly improve survival outcomes of patients with advanced ESCC, suggesting its potential as a standard treatment strategy.

With the advancement of modern research methods,machine learning(ML)algorithms have been widely applied in traditional Chinese medicine(TCM)diagnosis,transforming subjective syndrome differentiation into a more objective process,thereby providing a feasible pathway for the objectification and quantification of TCM diagnostics.However,challenges persist,including the dual"black-box"nature of disease-syndrome models(lacking interpretability),missing spatiotemporal dynamic data,and the"disconnect"between clinical phenotyping and molecular biomarker research.Focusing on non-alcoholic fatty liver disease(NAFLD),this study proposes a novel framework guided by the biological"multiple-hit"theory and TCM's"disease-syndrome-symptom-stage"approach,which involves:Constructing a mathematical model of NAFLD progression(simple steatosis→steatohepatitis→fibrosis→cirrhosis)via multi-level(phenotypic-cellular-molecular)network modules;And developing an interpretable multidimensional model integrating syndrome-imaging phenomics(macro)and metabolomics-derived biomarkers(micro)to enable personalized NAFLD diagnosis.

Objective:To explore the current status of self-management behavior in patients with inflammatory bowel disease and to construct a structural equation model to explore the mediating role of health literacy between discharge readiness and self-management behavior.Methods:From March to September 2023, 310 hospitalized patients with inflammatory bowel disease in the Tenth People ′s Hospital Affiliated to Tongji University were selected as the research subjects by the convenience sampling method. A cross-sectional survey was conducted using the general information questionnaire, the Health Literacy Management Scale, the Discharge Readiness Scale, and the Self-management Behavior Scale. Results:Eventually, 303 valid questionnaires were retrieved. Among the 303 patients, there were 190 males and 113 females, with an average age of (35.63 ± 16.41) years old. The total score of health literacy of patients with inflammatory bowel disease was (94.35 ± 10.78) points, indicating a health literacy compliance rate of 74.3%(225/303). The total score of discharge readiness was (79.52 ± 9.16) points, and the total score of self-management behavior was (135.22 ± 17.36) points. In patients with inflammatory bowel disease, the discharge readiness was positively correlated with the total score of health literacy ( r=0.748, P<0.01), the discharge readiness was positively correlated with the total score of self-management behavior ( r=0.821, P< 0.01), and the health literacy was positively correlated with the total score of self-management behavior ( r=0.767, P<0.01). Health literacy may serve as a mediating variable, partially explaining the relationship between discharge readiness and self-management behavior in patients with inflammatory bowel disease. The mediating effect accounted for 47.5% of the total effect. Conclusions:Discharge readiness not only positively impacts self-management behavior among patients with inflammatory bowel disease but also indirectly influences this behavior through health literacy. It is essential for clinical healthcare professionals to prioritize the assessment of discharge readiness and health literacy among these patients. By improving discharge readiness and enhancing health literacy, clinical healthcare professionals can effectively support patients with inflammatory bowel disease in their self-management efforts.

Objective:To develop a nutritional assessment scale for patients with inflammatory bowel disease and examine its reliability and validity for assessing nutritional literacy in patients with inflammatory bowel disease.Methods:Based on the Nutbeam health literacy stratification model and knowledge-attitude-practice model, a preliminary scale was developed through literature review, semi-structured interviews, expert consultation and pre-surveys. A convenient sampling method was used to select 376 inflammatory bowel disease patients admitted to Tenth People′s Hospital Affiliated to Tongji University from September, 2022 to April, 2023 for questionnaire surveys, and reliability and validity tests were conducted to form the final scale.Results:The nutritional assessment scale for inflammatory bowel disease included 39 items. Exploratory factor analysis identified five common factors: nutritional knowledge, nutritional attitudes, nutritional practices, information interaction ability, and information evaluation ability. These factors explained 65.431% of the total variance. The content validity index of the scale was 0.857, and the item-level content validity index ranged from 0.800 to 1.000. The Cronbach α coefficient of the scale was 0.869, and the Cronbach α coefficients of each dimension ranged from 0.847 to 0.922. Conclusions:The developed nutritional assessment scale for inflammatory bowel disease demonstrates good reliability and validity, allowing for effective evaluation of patients′nutritional status.