In the context of the digital-intelligent era of traditional Chinese medicine （TCM）， the lack of clinical thinking is a pressing issue that limits the overall effectiveness of TCM and talent cultivation. The "disease-pulse-syndrome-treatment differentiation" thinking model， originally developed by ZHANG Zhongjing in the Treatise on Cold Damage and Miscellaneous Diseases （Shang Han Za Bing Lun）， has served as a guideline and paradigm followed by generations of medical practitioners. This study aims to construct a digitalized "disease-pulse-syndrome-treatment differentiation" thinking system， develop a digital assessment system， and implement it for practical application. The goal is to recommend a digitalized assessment model for TCM and provide a reference for the integrated innovation of talent cultivation in medicine， education， and research. First， based on the complex diagnostic and treatment framework of the Treatise on Cold Damage Diseases （Shang Han Lun）， the research team previously established a "process" + "result" thinking model that included four processes and ten steps. This study integrates knowledge unit theory and digital technology to create a digital system for "disease-pulse-syndrome-treatment differentiation" with a dual-control model of "process control" and "result control". The system consists of 46 items across three categories： knowledge body （W=20%）， knowledge element （W=30%）， and knowledge element associations （W=50%）. Second， a mixed-methods research design was employed， combining qualitative and quantitative approaches. The Delphi method was used to establish hierarchical levels and screen items， while the analytic hierarchy process （AHP） was used to assign weights. Expert surveys were conducted to reach a consensus and further validate the rationale and necessity of the system. Finally， based on the system architecture and integrating key computer technologies， a digital assessment system for "disease-pulse-syndrome-treatment differentiation" was developed. The Treatise on Cold Damage Diseases （Shang Han Lun） was used as a case study to validate the system's feasibility. Statistical results showed that the difficulty level of the assessment question bank was moderate （DL ranging from 0.65 to 0.89）， with good discrimination （D>0.4）， and reasonable reliability and validity （Cronbach's α=0.84， KMO=0.72， Bartlett's test P<0.01）. The system can perform process-oriented evaluations of candidates' thinking in "disease-pulse-syndrome-treatment differentiation" and effectively achieve the goal of clinical thinking assessment through a combination of "process control" and "result control". The examination system offers three major advantages. It standardizes， objectifies， and streamlines the assessment of thought processes， facilitates the organic transformation of TCM education from outcome-based education to thinking-based education， and from exam-oriented education to competency-oriented education， and promotes the practical transformation of TCM assessments from qualitative to quantitative evaluation， as well as from theory to practice. In summary， this system not only represents a technological upgrade to traditional examinations but also empowers the cultivation and assessment of clinical talent in the digital-intelligent era， demonstrating broad application prospects.

Dry eye disease(DED)and depression(DEP), though anatomically and clinically distinct, show significant epidemiological, pathophysiological, and prognostic interplay. Their co-occurrence has risen sharply in recent years, yet the mechanisms driving this comorbidity remain under-investigated. This review systematically synthesizes current evidence, highlighting that pro-inflammatory cytokines, neuro-regulatory imbalance, mitochondrial dysfunction, and gut-microbiota dysbiosis constitute a shared molecular network, while sleep deprivation and antidepressant use further amplify the vicious cycle. By identifying limitations in existing studies, this review proposes future research directions to offer new theoretical and clinical insights for managing DED-DEP comorbidity.

BackgroundThe violent aggressive behaviors of patients with schizophrenia usually have the characteristics of suddenness， unpredictability， high severity， and great difficulty in prevention. Early identification and accurate assessment of their risk of violent aggression have significant clinical significance. ObjectiveTo construct a predictive model for the violence risk in hospitalized patients with schizophrenia， to identify the key factors influencing the occurrence of violent behavior in these patients， so as to provide references for clinical precise quantitative assessment and early intervention. MethodsA total of 200 patients with schizophrenia who were hospitalized at Taiyuan Psychiatric Hospital from March 2022 to September 2024 and met the diagnostic criteria of the International Classification of Diseases， eleventh edition （ICD-11） were collected to form the modeling cohort. They were randomly divided into a training set （n=140） and a test set （n=60） at a ratio of 7∶3. Based on the least absolute shrinkage and selection operator （LASSO） regression algorithm， the feature variables were screened and dimension-reduced. The support vector machine （SVM） from machine learning was selected for model training and prediction. The discrimination efficacy of the model was evaluated by the area under the receiver operating characteristic （ROC） curve （AUC）， accuracy， precision， sensitivity， specificity， F1 value， and Brier value. ResultsLASSO regression screening identified 16 feature variables. Pearson correlation analysis revealed a positive correlation between prior violent behavior frequency and clinical psychiatric symptom scores （r=0.580， P<0.01）， a positive correlation between hospitalization compliance and current disease status （r=0.550， P=0.003）， and a positive correlation between educational level and family per capita monthly income （r=0.367， P<0.01）. The SVM model achieved an AUC of 0.853， accuracy of 0.800， precision of 0.810， sensitivity of 0.895， specificity of 0.636， F1 value of 0.850， and Brier value of 0.168. ConclusionThe SVM model has a relatively high level of applicability and overall predictive performance in the assessment of violent risk in schizophrenia patients， which is helpful for the early identification of violent risks in such patients. ［Funded by Specialized Research Project for Enhancing the Competence of Health Professionals in Taiyuan City （number， Y2023006）］

ObjectiveTo establish an ultra-performance liquid fingerprint and multi-components determination method for Naomaili granules. To evaluate the quality of different batches by chemometrics， and the anti-neuroinflammatory effects of water extract and main components of Naomaili granules were tested in vitro. MethodsThe similarity and common peaks of 27 batches of Naomaili granules were evaluated by using Ultra performance liquid chromatography （UPLC） fingerprint detection. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） technology was used to determine the content of the index components in Naomaili granules and to evaluate the quality of different batches of Naomaili granules by chemometrics. LPS-induced BV-2 cell inflammation model was used to investigate the anti-neuroinflammatory effects of the water extract and main components of Naomaili granules. ResultsThe similarity of fingerprints of 27 batches of samples was > 0.90. A total of 32 common peaks were calibrated， and 23 of them were identified and assigned. In 27 batches of Naomaili granules， the mass fractions of 14 components that were stachydrine hydrochloride， leonurine hydrochloride， calycosin-7-O-glucoside， calycosin，tanshinoneⅠ， cryptotanshinone， tanshinoneⅡ_A， ginsenoside Rb₁， notoginsenoside R₁， ginsenoside Rg₁， paeoniflorin， albiflorin， lactiflorin， and salvianolic acid B were found to be 2.902-3.498， 0.233-0.343， 0.111-0.301， 0.07-0.152， 0.136-0.228， 0.195-0.390， 0.324-0.482， 1.056-1.435， 0.271-0.397， 1.318-1.649， 3.038-4.059， 2.263-3.455， 0.152-0.232， 2.931-3.991 mg∙g^-1， respectively. Multivariate statistical analysis showed that paeoniflorin， ginsenoside Rg₁， ginsenoside Rb₁ and staphylline hydrochloride were quality difference markers to control the stability of the preparation. The results of bioactive experiment showed that the water extract of Naomaili granules and the eight main components with high content in the prescription had a dose-dependent inhibitory effect on the release of NO in the cell supernatant. Among them， salvianolic acid B and ginsenoside Rb₁ had strong anti-inflammatory activity， with IC₅₀ values of （36.11±0.15） mg∙L^-1 and （27.24±0.54） mg∙L^-1， respectively. ConclusionThe quality evaluation method of Naomaili granules established in this study was accurate and reproducible. Four quality difference markers were screened out， and eight key pharmacodynamic substances of Naomaili granules against neuroinflammation were screened out by in vitro cell experiments.

Background Work-related musculoskeletal disorders (WMSDs) are a major occupational health concern, particularly among workers exposed to adverse ergonomic conditions. Manganese production involves heavy physical demands, yet research on WMSDs among manganese workers remains limited. Objective To investigate the prevalence and influencing factors of WMSDs among manganese workers in a manganese enterprise in Guangxi. Methods A cross-sectional survey was conducted from May to June 2024 on workers at a manganese factory in Guangxi. The Chinese Musculoskeletal Disorders Questionnaire was used to collect information on demographic characteristics, distribution of musculoskeletal symptoms, and work-related exposures. χ² test was applied to compare differences in positive WMSDs rates across groups, and logistic regression analysis was performed to identify associated factors. Results A total of 1476 workers were enrolled in the study after pre-determined inclusion and exclusion criteria. The overall prevalence of WMSDs was 34.15%. The most commonly affected body regions were the lower back (17.28%), neck (16.67%), and shoulders (13.82%). The results of logistic regression analysis indicated that female, older age, and education level of college or above were associated with a higher risk of WMSDs (P<0.05). Awkward working postures were significantly associated with WMSDs in corresponding body regions; in particular, awkward postures of the neck, upper limbs, trunk, and lower limbs were related to an increased risk of WMSDs in multiple body sites (P<0.05). In addition, poor lighting conditions, high workplace temperature, frequent or sustained arm support during work, and high job demands were associated with an increased risk of overall or site-specific WMSDs (P<0.05). Conclusion The high prevalence of WMSDs among manganese workers is closely associated with demographic characteristics, working postures, and work environment and organizational factors. Targeted ergonomic interventions focusing on high-risk body regions and key ergonomic exposures are warranted to reduce the risk of WMSDs among manganese workers.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

Objective To explore the application value of three mainstream large language models in the diagnosis, differential diagnosis, and treatment decision support of the primary diseases related to pediatric liver transplantation. Methods Seventy-nine cases of pediatric liver transplantation-related diseases diagnosed through pathological or clinical follow-up data were collected from Renji Hospital, Shanghai Jiao Tong University School of Medicine or published high-quality case reports. These cases covered 25 types of primary diseases such as cholestatic liver disease, metabolic diseases, and tumors. Standardized prompts were used to input the case information into the DeepSeek-R1, ChatGPT-4o and Grok-3 models, and the accuracy of their preliminary diagnosis and differential diagnosis based on basic clinical data was evaluated. The final diagnosis accuracy and the response time after supplementary examination were also assessed, as well as the completeness and rationality of their analysis of disease treatment principles. Results In the initial diagnosis and differential diagnosis stage, the comprehensive accuracy of DeepSeek-R1 was the highest [72.1%, 95% confidence interval (CI) 61.4% - 80.8%], and there was a statistically significant difference in the comprehensive accuracy of the three models for initial diagnosis (P = 0.008). After adding further examination information, the final diagnosis accuracy of the three models increased, with DeepSeek-R1 at 88.6% (95% CI 79.7% - 93.9%), ChatGPT-4o at 87.3% (95% CI 78.2% - 93.0%), and Grok-3 at 78.5% (95% CI 68.2% - 86.1%). There was no statistically significant difference among the three models (P = 0.05). The scores given by experts for the treatment principles showed good consistency (Kappa = 0.769). In addition, the response time of ChatGPT-4o is shorter than that of the other two models [(24 ± 7) s]. Conclusions Large language models demonstrate good efficacy in the diagnosis and treatment decision-making process of various pediatric liver diseases, have a good application prospect for auxiliary diagnosis and decision support, and are expected to help improve the accuracy and efficiency of clinical diagnosis and treatment of pediatric liver transplantation-related primary diseases.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

Objective: To explore a simple, effective, and safe method for excluding false positives and identifying infections by comprehensively evaluating blood donors with reactive HIV screening results, thereby providing a basis for developing management strategies for such donors. Methods: HIV testing data of blood donors from our laboratory from January 2022 to December 2024 were collected. The results of ELISA and nucleic acid testing (NAT) were combined with confirmatory results from the CDC and analyzed. Results: A total of 605 929 samples were tested for HIV over the three-year period, with 682 reactive samples (reactive rate: 11.25 per 10 000). All were sent to the CDC for Western blot (WB) confirmation, resulting in 53 confirmed positives ((confirmed positive rate: 7.77%). Among these, 619 samples showed isolated HIV Ag&Ab reactivity with non-reactive NAT (HIV Ag&Ab+-&HIV RNA or NAT NR), with a confirmed infection rate of 0%; 9 samples showed dual HIV Ag&Ab reactivity with non-reactive NAT (HIV Ag&Ab++&HIV RNA NR or NAT NR), also with 0% confirmed infection; 52 samples showed dual HIV Ag&Ab reactivity and reactive NAT (HIV Ag&Ab++&HIV RNA R or NAT R), all confirmed as positive (100% infection rate); and 2 HIV Ag&Ab dual-reactive samples without NAT detection were also confirmed infected (100%). For all four HIV Ag&Ab assays, the S/CO values in the true positive group with dual reactivity were significantly higher than those in the false-positive groups (P<0.05). The S/CO distributions for both single-reactive false positives and dual-reactive false positives were narrow, with the upper box (Q3, 75th percentile) below optimal cutoff values in all cases (The optimal cutoff values for the four reagents were 5.00, 11.67, 8.50, and 20.90, respectively). Conclusion: Blood donors with positive NAT results in HIV blood screening are permanently deferred. Donors with dual positive HIV Ag&Ab but negative NAT results are classified and managed based on the S/CO values of HIV Ag&Ab and the optimal screening thresholds. Donors with single positive HIV Ag&Ab but negative NAT results are placed under evaluation status and retain their eligibility to donate blood. Optimizing the management measures for blood donors and establishing a scientific stratified management and assessment mechanism can effectively maintain the stability of the blood donor team.

ObjectiveTo investigate the regulatory effects and underlying mechanisms of Liuhuang Zhike prescription （LHZK） on blood glucose in type 2 diabetic db/db mice based on the AMP-activated protein kinase/protein kinase B/glycogen synthase kinase-3β （AMPK/Akt/GSK-3β） signaling pathway. MethodsDb/db mice were used as the model animals， and db/m mice served as the blank control. Forty db/db mice were randomly divided into the model group， metformin group （0.14 g·kg^-1）， and low-， medium-， and high-dose （4.11， 8.21， 16.43 g·kg^-1） LHZK groups， with 8 mice in each group. The db/db mice in the metformin and LHZK groups were administered the corresponding drugs by gavage， while the blank control and model groups were given distilled water by gavage once daily for 8 consecutive weeks. Food intake， water consumption， body weight， and fasting blood glucose （FBG） were measured weekly. An automatic biochemical analyzer was used to determine glycated serum protein （GSP）， serum triglycerides （TG）， total cholesterol （TC）， high-density lipoprotein cholesterol （HDL-C）， and low-density lipoprotein cholesterol （LDL-C） levels. Hematoxylin-eosin （HE） staining was used to observe pathological morphological changes in the liver and pancreatic tissues. Oil red O staining was used to assess lipid accumulation in liver tissue. The anthrone colorimetric method was used to determine hepatic glycogen content. Real-time quantitative PCR （Real-time PCR） was used to detect the mRNA expression levels of insulin receptor substrate-1 （IRS-1）， Akt2， phosphoenolpyruvate carboxykinase （PEPCK）， and glucose-6-phosphatase （G6Pase） in liver tissue. Western blot was used to detect the protein expression of AMPKα， phosphorylated AMPKα （p-AMPKα）， GSK-3β， p-GSK-3β， glycogen synthase （GS）， and phosphorylated GS （p-GS） in liver tissue. ResultsCompared with the blank control group， the model group showed significantly increased food intake， water consumption， body weight， FBG， and GSP levels （P<0.01）. Pancreatic islets exhibited marked parenchymal cell hyperplasia and interstitial inflammatory cell infiltration. Liver tissue showed obvious steatosis， accompanied by a compensatory increase in hepatic glycogen content （P<0.01）. Hepatic G6Pase mRNA expression was increased， while IRS-1 and Akt2 mRNA expression levels were significantly decreased （P<0.01）. The p-AMPKα/AMPKα protein expression ratio showed a decreasing trend， whereas the p-GSK-3β/GSK-3β and p-GS/GS protein expression ratios were significantly increased （P<0.01）. Compared with the model group， food intake and water consumption showed decreasing trends in all treatment groups. Food intake was significantly reduced in the low- and high-dose LHZK groups and in the metformin group （P<0.05， P<0.01）， and water consumption was significantly reduced in the low-dose LHZK group and in the metformin group （P<0.05， P<0.01）. No statistically significant differences in body weight were observed among the LHZK groups， whereas body weight in the metformin group was significantly increased （P<0.05， P<0.01）. FBG showed a decreasing trend in all treatment groups， with significant decreases in the low-dose LHZK group and the metformin group （P<0.05， P<0.01）. GSP levels were significantly reduced in the low-dose LHZK group and in the metformin group （P<0.05， P<0.01）. Hepatic steatosis and pancreatic pathological injury were alleviated to varying degrees in all treatment groups. Hepatic glycogen content further increased in all treatment groups， with significant increases in the medium- and high-dose LHZK groups （P<0.05）. Real-time PCR results showed that all treatment groups downregulated the mRNA expression of G6Pase and PEPCK in the liver tissues of db/db mice， with significant downregulation of PEPCK mRNA in the low-dose LHZK and metformin groups （P<0.01）. Meanwhile， all treatment groups upregulated IRS-1 and Akt2 mRNA expression， with the most pronounced upregulation observed in the medium-dose LHZK group （P<0.01）. The p-AMPKα/AMPKα protein expression ratio was significantly increased in the low- and medium-dose LHZK groups （P<0.01）. The p-GSK-3β/GSK-3β protein expression ratio was significantly increased in all treatment groups （P<0.05， P<0.01）， whereas the p-GS/GS protein expression ratio was significantly decreased in all treatment groups （P<0.01）. ConclusionLHZK effectively reduces FBG and GSP levels in type 2 diabetic mice and improves hepatic steatosis and pancreatic islet pathological injury. Its hypoglycemic mechanism may be associated with regulation of the AMPK/Akt/GSK-3β signaling pathway and promotion of hepatic glycogen synthesis.