ObjectiveTo enhance teaching in postoperative cancer rehabilitation, this study developed an integrative Chinese-Western medicine postoperative oncology rehabilitation system, termed the medical oncology generative pre-trained transformer (MedOncoGPT). By introducing MedOncoGPT as an intelligent assistant, an integrated teaching model combining Chinese and Western medicine was established. The study evaluated its impact on students’ integrative clinical reasoning and practical abilities, providing support for instructional reform in related courses. MethodsUsing teaching resources as the knowledge base, MedOncoGPT was built upon the open-source ChatGLM model and incorporated Low-Rank Adaptation (LoRA) fine-tuning and retrieval-augmented generation (RAG) techniques to address postoperative integrative oncology scenarios. The system was applied in courses and clinical clerkships related to integrative oncology. In alignment with course objectives, a five-stage instructional process—pre-class preparation, in-class inquiry, simulated multidisciplinary consultation, clinical reinforcement, and teaching reflection—was designed to guide students in completing syndrome differentiation, comprehensive assessment, and follow-up planning within real or simulated case contexts. Comparative analyses of student engagement, syndrome differentiation thinking, evidence-based awareness, and interdisciplinary integration skills before and after the teaching reform were conducted using questionnaires, course assessments, classroom observations, and semi-structured interviews. ResultsFollowing the implementation of MedOncoGPT, students demonstrated improved performance in case analysis, prescription formulation, and integrative Chinese-Western medical evaluation compared with those receiving traditional instruction. Classroom participation and the relevance of student inquiries also increased. Self-assessment results indicated high levels of satisfaction with respect to clarity of integrative clinical reasoning, ability to retrieve and apply guideline-based evidence, and awareness of appropriate use of intelligent tools in clinical decision-making. More than 92% of students reported that the system facilitated understanding of abstract theoretical concepts presented in textbooks. Instructors noted that the system helped reduce lesson preparation time, enriched typical case materials and discussion scenarios, and promoted the translation of research findings into classroom teaching. Pilot data showed that, with MedOncoGPT assistance, the mean time for initial syndrome differentiation decreased from 18.4 min to 12.1 min, and the agreement rate increased from 68.3% to 82.5%. In the teaching pilot, the experimental group achieved a higher mean score on the final case analysis assessment than the control group (82.6 vs. 74.3). ConclusionThe integration of MedOncoGPT into teaching on postoperative integrative cancer rehabilitation enabled the establishment of a stable instructional process within existing curricula and enhanced students’ integrative clinical reasoning and evidence-based practice capabilities. The approach demonstrates positive potential for advancing the integration of research, clinical practice, and education and represents a valuable exploratory strategy for instructional reform in courses on integrative Chinese-Western medicine.

ObjectiveTo enhance teaching in postoperative cancer rehabilitation, this study developed an integrative Chinese-Western medicine postoperative oncology rehabilitation system, termed the medical oncology generative pre-trained transformer (MedOncoGPT). By introducing MedOncoGPT as an intelligent assistant, an integrated teaching model combining Chinese and Western medicine was established. The study evaluated its impact on students’ integrative clinical reasoning and practical abilities, providing support for instructional reform in related courses. MethodsUsing teaching resources as the knowledge base, MedOncoGPT was built upon the open-source ChatGLM model and incorporated Low-Rank Adaptation (LoRA) fine-tuning and retrieval-augmented generation (RAG) techniques to address postoperative integrative oncology scenarios. The system was applied in courses and clinical clerkships related to integrative oncology. In alignment with course objectives, a five-stage instructional process—pre-class preparation, in-class inquiry, simulated multidisciplinary consultation, clinical reinforcement, and teaching reflection—was designed to guide students in completing syndrome differentiation, comprehensive assessment, and follow-up planning within real or simulated case contexts. Comparative analyses of student engagement, syndrome differentiation thinking, evidence-based awareness, and interdisciplinary integration skills before and after the teaching reform were conducted using questionnaires, course assessments, classroom observations, and semi-structured interviews. ResultsFollowing the implementation of MedOncoGPT, students demonstrated improved performance in case analysis, prescription formulation, and integrative Chinese-Western medical evaluation compared with those receiving traditional instruction. Classroom participation and the relevance of student inquiries also increased. Self-assessment results indicated high levels of satisfaction with respect to clarity of integrative clinical reasoning, ability to retrieve and apply guideline-based evidence, and awareness of appropriate use of intelligent tools in clinical decision-making. More than 92% of students reported that the system facilitated understanding of abstract theoretical concepts presented in textbooks. Instructors noted that the system helped reduce lesson preparation time, enriched typical case materials and discussion scenarios, and promoted the translation of research findings into classroom teaching. Pilot data showed that, with MedOncoGPT assistance, the mean time for initial syndrome differentiation decreased from 18.4 min to 12.1 min, and the agreement rate increased from 68.3% to 82.5%. In the teaching pilot, the experimental group achieved a higher mean score on the final case analysis assessment than the control group (82.6 vs. 74.3). ConclusionThe integration of MedOncoGPT into teaching on postoperative integrative cancer rehabilitation enabled the establishment of a stable instructional process within existing curricula and enhanced students’ integrative clinical reasoning and evidence-based practice capabilities. The approach demonstrates positive potential for advancing the integration of research, clinical practice, and education and represents a valuable exploratory strategy for instructional reform in courses on integrative Chinese-Western medicine.

OBJECTIVE To investigate the equivalence of different decoction processes based on rat model of hypertension with liver-yang hyperactivity. METHODS Inductively coupled plasma mass spectrometry （ICP-MS） was used to compare the dissolution differences of inorganic elements in the powder-directly-decocted decoction versus the pieces-decocted-first decoction of Ostreae Concha- Haliotidis Concha- Margaritifera Concha drug pair. Six SD rats were included in the normal group. The spontaneously hypertensive rats were given Aconite decoction for six weeks to induce the hypertension model with liver-yang hyperactivity. After successful modeling， 48 rats were randomly divided into the model group， the captopril group [positive control， 8 mL/（kg·d） ] ， as well as low-， medium-， and high-dose groups of pieces decocted first or directly powder decocted [2.02， 4.05， 8.10 mL/（kg·d） ] ， with 6 rats in each group. Each group received the corresponding drug or equal volume of pure water intragastrically， once a day， for two consecutive weeks. After the last administration， the degree of irritability， facial temperature， pressure pain threshold， blood pressure， and pathological changes of the thoracic aorta were observed in each group. Serum nitric oxide （NO） and plasma angiotensin Ⅱ （Ang Ⅱ）， renin， and aldosterone （ALD） levels were also measured. RESULTS ICP-MS analysis results showed statistically significant differences in the contents of macroelements Li， Na， Mg， Ca， Mn， Ga， Sr， Mo， Cd， Sn， and Sb， between the powder-directly-decocted decoction and the pieces-decocted-first decoction （ P ＜0.05） ，the elements P， Cr， Fe， Ni， Zn， Hg， Tl， and Pb were not detected in either decoction. Animal experiments showed that after two weeks of administration， compared with the model group， the facial temperature， and blood pressure decreased in all treatment groups， while the pressure pain threshold increased； plasma levels of Ang Ⅱ， renin and ALD， as well as the serum level of NO were all decreased， and thoracic aortic media thickness was significantly reduced， most of the differences in the above indicators were statistically significant （ P ＜0.05 or P ＜0.01 or P ＜0.001）. Pathological observation showed improvement in thoracic aortic pathological injury. CONCLUSIONS The powder-directly-decocted process for the Ostreae Concha- Haliotidis Concha- Margaritifera Concha drug pair significantly promotes the dissolution of key elements such as Ca， Mg， and Sr without increasing the dissolution of harmful elements. It is equivalent to the traditional pieces-decocted-first in alleviating liver-yang hyperactivity syndrome， lowering blood pressure， and protecting the vascular endothelium， and even shows better performance in some indicators.

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.

ObjectiveTo explore the mechanism by which Sini San （SNS） inhibits ferroptosis， alleviates inflammation and myocardial injury， and improves myocardial infarction （MI）. MethodsThe active ingredients of SNS were obtained by searching the Traditional Chinese Medicine System Pharmacology Platform （TCMSP） database， its target sites were predicted using the SwissTargetPrediction Database， and the core components were screened out using the CytoNCA plug-in. The targets of MI and ferroptosis were obtained by using GeneCards， Online Mendelian Inheritance in Man （OMIM） database， DrugBank， Therapeutic Target Database （TTD）， FerrDb database and literature review， respectively. The intersection of these targets of SNS-MI-ferroptosis was plotted as a Venn diagram. The protein-protein interaction （PPI） network was constructed using the STRING database， and the visualization graph was prepared using Cytoscape. The core targets were screened out using the CytoNCA plug-in， and the biological functions were clustered by the MCODE plug-in. Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were performed using the David database. Molecular docking was performed using AutoDock and visualized with PyMOL2.5.2. The Kunming mice were randomly divided into the control group， the model group， the SNS group， and the trimetazidine （TMZ） group. The mice were subcutaneously injected with isoprenaline （ISO， 5 mg·kg^-1·d^-1） to establish an MI model. The drug was continuously intervened for 7 days. The ST-segment changes were recorded by electrocardiogram （ECG）， and the tissue morphology changes were observed by hematoxylin-eosin （HE） staining. Cardiomyocyte ferroptosis was investigated by transmission electron microscopy. Serum creatine kinase （CK）， creatine kinase isoenzyme （CK-MB）， lactate dehydrogenase （LDH）， reduced glutathione （GSH）， and malondialdehyde （MDA） levels were detected by biochemical assay. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum levels of interleukin （IL）-6 and 4-hydroxynonenal （4-HNE）. Immunohistochemical staining was employed to detect IL-6 and phosphorylated signal transducer and transcription activator 3 （p-STAT3） in cardiac tissues. Western blot was used to detect STAT3 and p-STAT3 in cardiac tissues. Real-time PCR was used to detect the levels of IL-6， IL-18， solute carrier family 7 member 11 （SLC7A11）， arachidonic acid 15-lipoxygenase （ALOX15）， and glutathione peroxidase 4 （GPx4） in cardiac tissues. ResultsA total of 121 active ingredients of SNS were obtained， and 58 potential targets of SNS in the treatment of MI by regulating ferroptosis were screened. The three protein modules with a score5 were mainly related to the inflammatory response. The GO function was mainly related to inflammation， and KEGG enrichment analysis showed that SNS mainly regulated ferroptosis- and inflammation- related signaling pathways. Molecular docking indicated that the core component had a higher binding force to the target site. Animal experiments confirmed that SNS reduced the level of p-STAT3 （P0.01）， down-regulated the expression of ALOX15 mRNA （P0.01）， up-regulated the level of serum GSH， and the expressions of SLC7A11 and GPx4 mRNA， reduced MDA and 4-HNE levels （P0.05， P0.01）. Additionally， SNS improved the mitochondrial injury induced by cardiomyocyte ferroptosis， reduced the area of MI， alleviated inflammation and myocardial injury， lowered the levels of serum CK， CK-MB， LDH， IL-6， and the mRNA expression levels of IL-16 and IL-18 （P0.05）， and improved ST segment elevation. ConclusionSNS can reduce ISO-induced STAT3 phosphorylation levels， inhibit ferroptosis in cardiomyocytes， alleviate inflammation and myocardial injury， thereby improving MI.

ObjectiveTo explore the mechanism by which Sini San （SNS） inhibits ferroptosis， alleviates inflammation and myocardial injury， and improves myocardial infarction （MI）. MethodsThe active ingredients of SNS were obtained by searching the Traditional Chinese Medicine System Pharmacology Platform （TCMSP） database， its target sites were predicted using the SwissTargetPrediction Database， and the core components were screened out using the CytoNCA plug-in. The targets of MI and ferroptosis were obtained by using GeneCards， Online Mendelian Inheritance in Man （OMIM） database， DrugBank， Therapeutic Target Database （TTD）， FerrDb database and literature review， respectively. The intersection of these targets of SNS-MI-ferroptosis was plotted as a Venn diagram. The protein-protein interaction （PPI） network was constructed using the STRING database， and the visualization graph was prepared using Cytoscape. The core targets were screened out using the CytoNCA plug-in， and the biological functions were clustered by the MCODE plug-in. Gene Ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were performed using the David database. Molecular docking was performed using AutoDock and visualized with PyMOL2.5.2. The Kunming mice were randomly divided into the control group， the model group， the SNS group， and the trimetazidine （TMZ） group. The mice were subcutaneously injected with isoprenaline （ISO， 5 mg·kg^-1·d^-1） to establish an MI model. The drug was continuously intervened for 7 days. The ST-segment changes were recorded by electrocardiogram （ECG）， and the tissue morphology changes were observed by hematoxylin-eosin （HE） staining. Cardiomyocyte ferroptosis was investigated by transmission electron microscopy. Serum creatine kinase （CK）， creatine kinase isoenzyme （CK-MB）， lactate dehydrogenase （LDH）， reduced glutathione （GSH）， and malondialdehyde （MDA） levels were detected by biochemical assay. Enzyme-linked immunosorbent assay （ELISA） was used to detect serum levels of interleukin （IL）-6 and 4-hydroxynonenal （4-HNE）. Immunohistochemical staining was employed to detect IL-6 and phosphorylated signal transducer and transcription activator 3 （p-STAT3） in cardiac tissues. Western blot was used to detect STAT3 and p-STAT3 in cardiac tissues. Real-time PCR was used to detect the levels of IL-6， IL-18， solute carrier family 7 member 11 （SLC7A11）， arachidonic acid 15-lipoxygenase （ALOX15）， and glutathione peroxidase 4 （GPx4） in cardiac tissues. ResultsA total of 121 active ingredients of SNS were obtained， and 58 potential targets of SNS in the treatment of MI by regulating ferroptosis were screened. The three protein modules with a score5 were mainly related to the inflammatory response. The GO function was mainly related to inflammation， and KEGG enrichment analysis showed that SNS mainly regulated ferroptosis- and inflammation- related signaling pathways. Molecular docking indicated that the core component had a higher binding force to the target site. Animal experiments confirmed that SNS reduced the level of p-STAT3 （P0.01）， down-regulated the expression of ALOX15 mRNA （P0.01）， up-regulated the level of serum GSH， and the expressions of SLC7A11 and GPx4 mRNA， reduced MDA and 4-HNE levels （P0.05， P0.01）. Additionally， SNS improved the mitochondrial injury induced by cardiomyocyte ferroptosis， reduced the area of MI， alleviated inflammation and myocardial injury， lowered the levels of serum CK， CK-MB， LDH， IL-6， and the mRNA expression levels of IL-16 and IL-18 （P0.05）， and improved ST segment elevation. ConclusionSNS can reduce ISO-induced STAT3 phosphorylation levels， inhibit ferroptosis in cardiomyocytes， alleviate inflammation and myocardial injury， thereby improving MI.

To summarize the clinical experience of treating idiopathic olfactory disorder from the perspective of heart and lungs. It is believed that the sense of olfaction is based on the nose， rooted in the heart， functioning through the lungs， and conveyed by pectoral qi. The key pathogenesis of idiopathic olfactory disorder lies in the accumulation of pathogenic factors in the heart and lungs， blood vessel obstruction， the failure of the lungs to disperse and descend， and the loss of control of pectoral qi. In treatment， internal and external therapies could be combined. The internal therapy can correct the imbalance of the zang-fu organs， using the self-prescribed Bilong Formula （鼻聋方） to dispel wind and diffuse the lung， invigorate blood and relieving stuffy orifices； the external therapy can clear nasal obstruction， supplemented by intradermal needle embedding at three acupoints， bilateral Yingxiang （LI 20）， bilateral Shangyingxiang （EX-HN 8） and Yintang （GV 29）， and integrated olfactory training for comprehensive treatment.

To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

BackgroundLow medication compliance among patients with severe mental disorders increases the disease burden on both the patients' families and the society. Medication adherence is influenced by numerous factors. Traditional methods such as Logistic regression struggle to quantify the importance of these factors. By introducing Extreme Gradient Boosting （XGBoost） combined with Shapley Additive Explanations （SHAP）， enables the quantification of the relative contribution weights of each factor， providing support for identifying the core influencing factors. ObjectiveTo explore the influencing factors of medication adherence among patients with severe mental disorders in Zhuhai， aiming to provide references for optimizing patient management strategies. MethodsExtract the data of patients with severe mental disorders who were registered on the mental health system platform in Zhuhai City from January 1， 2023 to March 31， 2025. A total of 9 329 patients were finally included for analysis. Influencing factors were screened using univariate analysis and multivariate logistic regression analysis， and an XGBoost model combined with the SHAP algorithm was constructed to quantify the importance of each influencing factor. ResultsAmong 9 329 patients， 8 446 demonstrated medication adherence， yielding an adherence rate of 90.53%. Multivariable analysis identified several risk factors significantly associated with medication non-adherence， being unmarried （OR=1.237， 95% CI： 1.019–1.502） or divorced （OR=1.389， 95% CI： 1.038–1.832）， a diagnosis of mental retardation with psychiatric disorders （OR=3.025， 95% CI： 2.402–3.796） or paranoid psychosis （OR=5.117， 95% CI： 3.086–8.299）， a disease duration of 2–4 years （OR=1.355， 95% CI： 1.085–1.696）， 4–6 years （OR=2.143， 95% CI： 1.671–2.747）， or >6 years （OR=1.681， 95% CI： 1.365–2.079）， lack of guardian subsidies （OR=1.412， 95% CI： 1.099–1.801）， absence of a disability certificate （OR=1.900， 95% CI： 1.588–2.282）， not being enrolled in care and support groups （OR=1.384， 95% CI： 1.183–1.617） or community services （OR=1.313， 95% CI： 1.042–1.645）， and not cohabiting with a guardian （OR=1.257， 95% CI： 1.048–1.501）. Conversely， the enrollment in special outpatient disease programs （OR=0.716， 95% CI： 0.609–0.842） and a family history of mental illness （OR=0.713， 95% CI： 0.503–0.982） were identified as protective factors. The XGBoost model exhibited robust predictive performance， with a sensitivity of 0.433， specificity of 0.944， accuracy of 0.891， Area Under the Curve （AUC） of 0.837， and F1 value of 0.449. Feature importance ranking indicated that the top three factors were disease duration， diagnosis， and the acquisition of disability certificates. ConclusionPolicy-based support （acquisition of disability certificates， special outpatient disease enrollment） and clinical disease characteristics （disease duration， diagnosis type） are key factors affecting medication adherence among patients with severe mental disorders in Zhuhai City. ［Funded by Zhuhai Medical Research Project （number， 2220009000281）］