ObjectiveThe essence of syndrome manifestation recognition in traditional Chinese medicine (TCM) is to infer the body’s latent pathogenesis state from clinical observational information, rather than to perform simple label matching. However, previous studies have largely modeled this task as syndrome pattern classification within a fixed label space, which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning, and is also insufficient to capture the openness, semantic variability, and cross-disease reusability of syndrome manifestation expression. This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought (PR-CoT) supervision into large language models (LLMs) could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer. MethodsSyndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information (X)→pathogenesis structure (Z)→syndrome pattern output (Y), where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment. Within this framework, a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders. After preprocessing, information extraction, manual proofreading, and data cleaning, the dataset comprised 4 800 training cases, 400 development cases, and 400 test cases. Each sample was annotated with a structured PR-CoT consisting of three progressive levels: clinical information summarization, comprehensive pathogenesis analysis, and syndrome pattern output. Supervised fine-tuning was conducted on open-source LLMs, with an end-to-end model serving as the baseline. Qwen3-32B was used as the primary experimental model, and Qwen3-14B as the scale comparison model. A progressive multidimensional evaluation framework was further established, comprising a structural parsing level, a semantic similarity level, and an expert blind review level. At the structural parsing level, syndrome pattern expressions were decomposed into structural elements and evaluated using Precision, Recall, F1 score, and Jaccard similarity. At the semantic similarity level, independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns. At the expert blind review level, three TCM experts independently evaluated model outputs on two dimensions: syndrome differentiation consistency and terminology standardization of syndrome patterns. In addition, zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets. ResultsAt the structural parsing level, PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components. Compared with the corresponding baselines, neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision. In contrast, at the semantic similarity level, PR-CoT supervision produced stable positive gains across different model scales and evaluation systems. The average semantic score of Qwen3-32B increased from 6.425 8 in the baseline model to 6.585 0 after PR-CoT supervision, and that of Qwen3-14B increased from 5.870 0 to 5.964 2. At the expert blind review level, the overall score of Qwen3-32B (PR-CoT) was 7.026 0±0.107 7, higher than 6.416 3±0.288 9 for its baseline. In zero-shot cross-disease testing, the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets, indicating a certain degree of transferability. ConclusionThe benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility, rather than in improved hard matching of structural elements. These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures, rather than as a classification task within a traditional fixed label space. By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework, this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment, interpretability, and multi-level evaluation.

ObjectiveThe essence of syndrome manifestation recognition in traditional Chinese medicine (TCM) is to infer the body’s latent pathogenesis state from clinical observational information, rather than to perform simple label matching. However, previous studies have largely modeled this task as syndrome pattern classification within a fixed label space, which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning, and is also insufficient to capture the openness, semantic variability, and cross-disease reusability of syndrome manifestation expression. This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought (PR-CoT) supervision into large language models (LLMs) could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer. MethodsSyndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information (X)→pathogenesis structure (Z)→syndrome pattern output (Y), where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment. Within this framework, a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders. After preprocessing, information extraction, manual proofreading, and data cleaning, the dataset comprised 4 800 training cases, 400 development cases, and 400 test cases. Each sample was annotated with a structured PR-CoT consisting of three progressive levels: clinical information summarization, comprehensive pathogenesis analysis, and syndrome pattern output. Supervised fine-tuning was conducted on open-source LLMs, with an end-to-end model serving as the baseline. Qwen3-32B was used as the primary experimental model, and Qwen3-14B as the scale comparison model. A progressive multidimensional evaluation framework was further established, comprising a structural parsing level, a semantic similarity level, and an expert blind review level. At the structural parsing level, syndrome pattern expressions were decomposed into structural elements and evaluated using Precision, Recall, F1 score, and Jaccard similarity. At the semantic similarity level, independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns. At the expert blind review level, three TCM experts independently evaluated model outputs on two dimensions: syndrome differentiation consistency and terminology standardization of syndrome patterns. In addition, zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets. ResultsAt the structural parsing level, PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components. Compared with the corresponding baselines, neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision. In contrast, at the semantic similarity level, PR-CoT supervision produced stable positive gains across different model scales and evaluation systems. The average semantic score of Qwen3-32B increased from 6.425 8 in the baseline model to 6.585 0 after PR-CoT supervision, and that of Qwen3-14B increased from 5.870 0 to 5.964 2. At the expert blind review level, the overall score of Qwen3-32B (PR-CoT) was 7.026 0±0.107 7, higher than 6.416 3±0.288 9 for its baseline. In zero-shot cross-disease testing, the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets, indicating a certain degree of transferability. ConclusionThe benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility, rather than in improved hard matching of structural elements. These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures, rather than as a classification task within a traditional fixed label space. By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework, this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment, interpretability, and multi-level evaluation.

Objective: To explore whether digital facial and tongue diagnostic technologies can support the assessment of coronary heart disease (CHD) patients for coronary artery stenosis severity, and examine potential associations between digital tongue diagnosis features and myocardial biomarkers. Methods: The TFDA-1 face and tongue diagnosis instrument and the TDAS analysis system were used to perform intelligent visual examination and analysis of the facial and tongue in CHD patients who attended the Department of Cardiology at Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine between October 2, 2023 and July 31, 2024. Variables were screened using principal component analysis (PCA) and multicollinearity analysis to construct four machine learning models, including random forest, LightGBM, decision tree, and naive Bayes, for the early prediction of coronary artery stenosis severity. Model performance metrics, including sensitivity, specificity, precision, F1 score, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC), were evaluated. Visual analyses were performed using the SHapley Additive exPlanations (SHAP) interpreter and decision curve analysis. For patients after percutaneous coronary intervention (PCI), a conceptual model linking cardiac biomarkers and tongue diagnosis was constructed using the partial least squares structural equation modeling (PLS-SEM), and its validity was assessed. Results: A total of 459 CHD patients were enrolled and assigned to a PCI group and a non-PCI group (which comprised two subgroups: mild stenosis or less group, moderate stenosis or greater group). For sublingual vein (SV) features, the PCI group had lower SV-a and SV-b than the other groups (P < 0.01 and P < 0.05, respectively). For tongue surface features, the PCI group had significantly higher tongue body (TB)-L, TB-a, and TB-b (P < 0.05, P < 0.01, and P < 0.001, respectively), as well as higher tongue coating (TC)-a and TC-b (P < 0.01 and P < 0.001, respectively). Age, SV-a, SV-b, creatine kinase-myocardial band (CK-MB), CK, TC-a, lip-L, and lip-b were incorporated in the machine learning models. The random forest model performed best, with an AUC of 0.924, an F1 score of 0.839, precision of 0.807, accuracy of 0.864, sensitivity of 0.873, and specificity of 0.839. Decision curve analysis indicated that both LightGBM and random forest had clinical utility. PLS-SEM confirmed the pathway relationships: myocardial biomarkers → TB and myocardial biomarkers → TC (coefficient = – 0.238, t = 2.239, P = 0.025, and coefficient = – 0.270, t = 2.522, P = 0.012, respectively). Conclusion This study developed a noninvasive early warning model for coronary artery stenosis in patients with CHD. It applied PLS-SEM to investigate the association between post-PCI cardiac biomarkers and tongue diagnosis, and validated the proposed association chain. These findings suggest that intelligent traditional Chinese medicine (TCM) visual diagnosis integrated with modern digital technology may support CHD risk assessment and comprehensive health management.

Objective: To understand the moderating effect of cohabitation with parents on the association between peer dating behavior and sexual behaviors among secondary vocational school students, so as to provide a scientific basis for preventing sexual behaviors among secondary vocational school students. Methods: From March to April 2021, an electronic questionnaire survey was conducted among 3 180 students from 6 vocational schools in Shanghai (urban, suburban, exurban) and Shaanxi (Shangluo, Ankang, Baoji) using cluster sampling. Spearman correlation analysis was used to investigate the relationship of cohabitation with parents, peer dating behavior and sexual behaviors among secondary vocational school students. Binary Logistic regression analysis was performed to investigate the role of cohabitation with parents on peer dating behavior and sexual behaviors among secondary vocational students. Results: There was a significant negative between cohabitation with parents and sexual ( r =-0.04); and there was a positive correlation between peer dating behavior and sexual behaviors ( r =0.24), as well as cohabitation with parents and peer dating behavior ( r =0.04)( P <0.05). Multivariable Logistic regression analysis showed an association between peer dating behavior and the occurrence of sexual behaviors ( OR=2.79-12.95, P <0.05). Cohabitation with parents played a moderating role in the association between peer dating behavior and sexual behaviors, and a signification interaction was found between cohabitation with parents and reporting that a small part or about half of their peers had dating behavior ( OR =0.48, P <0.05). Conclusions The more peers dating behavior are associated with a higher risk of sexual behaviors among secondary vocational school students, and cohabitation with parents can partly reduce this risk. School and family sexuality education for secondary vocational students should be strengthened to improve their interpersonal skills and decision-making, and ability to resist peer pressure, so as to reduce their risk of sexual behaviors.

Objective:This study aims to investigate the differences in hemostatic efficacy and patient comfort between an innovative domestically produced biodegradable nasal packing sponge and a traditional absorbent sponge following endoscopic nasal surgery. Methods:A prospective, randomized controlled trial design was utilized, including 30 patients who were divided into two groups according to random allocation, each receiving one of the two types of nasal packing. The study assessed the hemostatic efficacy, comfort, and safety of the materials by comparing the rates of no bleeding within 24 hours after packing, re-bleeding rates after 48 hours, pain ratings in the head and nasal areas, scores on a visual analog scale for nasal ocular symptoms, and safety indicators between the two groups. Results:The rates of no bleeding within 24 hours post-packing were 73.33% for both the experimental and control groups, with a no-bleeding rate of 100% after 48 hours in both groups. The pain rating in the head and nasal areas at various times post-packing was Grade Ⅰ（100%） in both groups, with no statistically significant difference（P=1.000）. The experimental groups sneezing score on the day of packing was（0.73±1.03）, lower than the control groups（2.27±1.67）, （P=0.007）; after 48 hours, the experimental groups sneezing score was（0.67±0.98）, also lower than the control groups（1.67±1.18）, （P=0.019）. There was no significant difference between the two groups in the Lund-Kennedy scoring during endoscopic examinations at the screening period, 7 days, 1 month, and 3 months post-packing（P>0.05）. Laboratory tests for other examination indicators were normal in both groups. Conclusion:The innovative domestically produced biodegradable nasal packing sponge not only provides hemostatic efficacy comparable to imported materials but also significantly improves patient comfort after surgery. It represents an economical and effective choice for nasal packing materials.
Humans ; Prospective Studies ; Surgical Sponges ; Endoscopy/methods* ; Male ; Female ; Epistaxis/prevention & control* ; Middle Aged ; Nasal Surgical Procedures/methods* ; Adult

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract, which increases the incidence of colorectal cancer (CRC). In the pathophysiology of IBD, ubiquitination/deubiquitination plays a critical regulatory function. Josephin domain containing 2 (JOSD2), a deubiquitinating enzyme, controls cell proliferation and carcinogenesis. However, its role in IBD remains unknown. Colitis mice model developed by dextran sodium sulfate (DSS) or colon tissues from individuals with ulcerative colitis and Crohn's disease showed a significant upregulation of JOSD2 expression in the macrophages. JOSD2 deficiency exacerbated the phenotypes of DSS-induced colitis by enhancing colon inflammation. DSS-challenged mice with myeloid-specific JOSD2 deletion developed severe colitis after bone marrow transplantation. Mechanistically, JOSD2 binds to the C-terminal of inosine-5'-monophosphate dehydrogenase 2 (IMPDH2) and preferentially cleaves K63-linked polyubiquitin chains at the K134 site, suppressing IMPDH2 activity and preventing activation of nuclear factor kappa B (NF-κB) and inflammation in macrophages. It was also shown that JOSD2 knockout significantly exacerbated increased azoxymethane (AOM)/DSS-induced CRC, and AAV6-mediated JOSD2 overexpression in macrophages prevented the development of colitis in mice. These outcomes reveal a novel role for JOSD2 in colitis through deubiquitinating IMPDH2, suggesting that targeting JOSD2 is a potential strategy for treating IBD.

OBJECTIVES: To investigate the mechanism by which transforming growth factor‑β (TGF‑β) regulates major histocompatibility complex class I (MHC-I) expression in hepatocellular carcinoma (HCC) cells and its role in immune evasion of HCC. METHODS: HCC cells treated with TGF‑β alone or in combination with SB-431542 (a TGF-β type I receptor inhibitor) were examined for changes in MHC-I expression using RT-qPCR and Western blotting. A RNA interference experiment was used to explore the role of miR-23a-3p/IRF1 signaling in TGF‑β‑mediated regulation of MHC-I. HCC cells with different treatments were co-cultured with human peripheral blood mononuclear cells (PBMCs), and the changes in HCC cell proliferation was assessed using CCK-8 and colony formation assays. T-cell cytotoxicity in the co-culture systems was assessed with lactate dehydrogenase (LDH) release and JC-1 mitochondrial membrane potential assays, and T-cell activation was evaluated by flow cytometric analysis of CD69 cells and ELISA for TNF-α secretion. RESULTS: TGF‑β treatment significantly suppressed MHC-I expression in HCC cells and reduced T-cell activation, leading to increased tumor cell proliferation and decreased HCC cell death in the co-culture systems. Mechanistically, TGF-β upregulated miR-23a-3p, which directly targeted IRF1 to inhibit MHC-I transcription. Overexpression of miR-23a-3p phenocopied TGF‑β‑induced suppression of IRF1 and MHC-I. CONCLUSIONS We reveal a novel immune escape mechanism of HCC, in which TGF‑β attenuates T cell-mediated antitumor immunity by suppressing MHC-I expression through the miR-23a-3p/IRF1 signaling axis.
Humans ; MicroRNAs/genetics* ; Carcinoma, Hepatocellular/metabolism* ; Liver Neoplasms/metabolism* ; Interferon Regulatory Factor-1/metabolism* ; Transforming Growth Factor beta/metabolism* ; Signal Transduction ; Histocompatibility Antigens Class I/metabolism* ; Cell Line, Tumor ; Tumor Escape ; Coculture Techniques

General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
Animals ; Machine Learning ; Electroencephalography/methods* ; Ketamine/administration & dosage* ; Rats ; Male ; Propofol/administration & dosage* ; Rats, Sprague-Dawley ; Anesthesia, General/methods* ; Brain/physiology* ; Intraoperative Neurophysiological Monitoring/methods*