The digitization of traditional Chinese medicine (TCM) has generated vast amounts of data. However, these data are characterized by significant heterogeneity and complex semantic structures, posing substantial challenges for systematic integration and intelligent analysis, and limiting its potential for modern clinical and computational research. To address the challenges posed by the high heterogeneity and complex structure in TCM data, we designed and developed the TCM Data Hub platform, which is powered by the YiYuan large language models (LLMs). This platform aims to enhance intelligent data processing capabilities and unlock the potential for clinical application of TCM data through systematic integration and efficient utilization, thereby bridging the gap between traditional knowledge and modern computational research. This study first analyzed the heterogeneity and complexity of TCM information with respect to data types, structures, and semantics. A standardized data framework was constructed to enhance data integration and interoperability. Based on the TCM Intelligent Computing Platform of the China Academy of Chinese Medical Sciences, we trained the YiYuan LLMs to acquire domain-specific semantic understanding of TCM, thereby improving the platform’s comprehension of specialized terminology and knowledge systems. Leveraging the natural language processing capabilities of the LLMs, we developed a human-in-the-loop data processing system to enable efficient extraction, cleansing, and structured organization of TCM data. In addition, utilizing Vue and Java technologies, we developed multiple LLM-powered intelligent agents and systems, including a human-in-the-loop data processing system, as well as automated prescription mining and network pharmacology analysis agents. Task-specific agents tailored to TCM data processing were developed to enhance the model’s effectiveness in clinical knowledge discovery. System functionality and platform infrastructure were implemented using Java and Vue technologies.The TCM Data Hub platform has completed system construction and core functionality implementation. It supported integrated management and efficient access to 8 key types of TCM data: prescriptions, materia medica, ingredients, targets, diseases (Western medicine), diseases (TCM), syndromes, and therapeutic methods. The human-in-the-loop data processing system achieved an accuracy of 95.34% in structuring TCM data and supported annotation for data requiring manual labeling. The intelligent agent-driven big-data analytics module enabled 1-click, end-to-end workflows for TCM prescription mining, herb-syndrome association analysis, network pharmacology, and molecular biology research, completing a full data mining task in approximately 30 minutes. Users can interact with and manipulate data through a visual front-end interface. The system demonstrated stable performance, strong scalability, and a user-friendly experience. Empowered by the YiYuan LLMs, the TCM Data Hub platform significantly improves the accessibility, usability, and intelligence of TCM data. It effectively bridges traditional TCM knowledge with modern intelligent technologies, providing robust data support and intelligent tools for TCM research and clinical applications.

Objective To address the need for rapid review of automated organ-at-risk (OAR) contouring for head-and-neck cancer in online adaptive radiotherapy, this study used dosimetric indices as the reference standard to evaluate the suitability of geometric similarity metrics and to develop a rapid screening model that balances the risk of missed errors with review efficiency. Methods This retrospective study included 29 patients with head-and-neck radiotherapy, yielding 243 pairs of OAR contours (auto-generated vs. manual). Geometric similarity metrics, including the Dice similarity coefficient (Dice), the 95th percentile Hausdorff distance (HD95), and the maximum Hausdorff distance (HD100), were computed between the two contour sets. Using the dose distributions from clinical treatment plans, dosimetric differences between the two contour sets were calculated for key indices, including mean dose (D_mean) and maximum dose (D_max). Correlations between geometric metrics and dosimetric differences were analyzed. Dosimetric discrepancy events were defined as Y_3Gy using a threshold of 3 Gy. Within a univariable logistic regression triage framework, each geometric metric was used as an independent variable to estimate the probability of Y_3Gy. Discriminative performance was assessed using receiver operating characteristic (ROC) curves and precision-recall (PR) curves, with the area under the curve (AUC) including ROC-AUC and PR-AUC. Practical operating thresholds were determined via threshold sweeping. Results Geometric similarity metrics showed weak-to-moderate correlations with dosimetric differences; compared with maximum dose, correlations with mean dose differences were more consistent. Among the evaluated metrics, HD95 achieved the best classification performance for Y_3Gy. Threshold sweeping suggested that an HD95 threshold in the range of 4-9 mm can balance the risk of missed discrepancies with the efficiency of automated contour quality assurance (QA). Conclusion Several geometric similarity metrics demonstrated only weak-to-moderate associations with dosimetric differences. For head-and-neck OAR contour QA in this study, HD95 provided the best discriminative performance and can support rapid triage of automated contours, with a practical operating threshold range of 4-9 mm, potentially improving the efficiency of adaptive radiotherapy workflows.

Objective To address the need for rapid review of automated organ-at-risk (OAR) contouring for head-and-neck cancer in online adaptive radiotherapy, this study used dosimetric indices as the reference standard to evaluate the suitability of geometric similarity metrics and to develop a rapid screening model that balances the risk of missed errors with review efficiency. Methods This retrospective study included 29 patients with head-and-neck radiotherapy, yielding 243 pairs of OAR contours (auto-generated vs. manual). Geometric similarity metrics, including the Dice similarity coefficient (Dice), the 95th percentile Hausdorff distance (HD95), and the maximum Hausdorff distance (HD100), were computed between the two contour sets. Using the dose distributions from clinical treatment plans, dosimetric differences between the two contour sets were calculated for key indices, including mean dose (D_mean) and maximum dose (D_max). Correlations between geometric metrics and dosimetric differences were analyzed. Dosimetric discrepancy events were defined as Y_3Gy using a threshold of 3 Gy. Within a univariable logistic regression triage framework, each geometric metric was used as an independent variable to estimate the probability of Y_3Gy. Discriminative performance was assessed using receiver operating characteristic (ROC) curves and precision-recall (PR) curves, with the area under the curve (AUC) including ROC-AUC and PR-AUC. Practical operating thresholds were determined via threshold sweeping. Results Geometric similarity metrics showed weak-to-moderate correlations with dosimetric differences; compared with maximum dose, correlations with mean dose differences were more consistent. Among the evaluated metrics, HD95 achieved the best classification performance for Y_3Gy. Threshold sweeping suggested that an HD95 threshold in the range of 4-9 mm can balance the risk of missed discrepancies with the efficiency of automated contour quality assurance (QA). Conclusion Several geometric similarity metrics demonstrated only weak-to-moderate associations with dosimetric differences. For head-and-neck OAR contour QA in this study, HD95 provided the best discriminative performance and can support rapid triage of automated contours, with a practical operating threshold range of 4-9 mm, potentially improving the efficiency of adaptive radiotherapy workflows.

Objective To address the need for rapid review of automated organ-at-risk (OAR) contouring for head-and-neck cancer in online adaptive radiotherapy, this study used dosimetric indices as the reference standard to evaluate the suitability of geometric similarity metrics and to develop a rapid screening model that balances the risk of missed errors with review efficiency. Methods This retrospective study included 29 patients with head-and-neck radiotherapy, yielding 243 pairs of OAR contours (auto-generated vs. manual). Geometric similarity metrics, including the Dice similarity coefficient (Dice), the 95th percentile Hausdorff distance (HD95), and the maximum Hausdorff distance (HD100), were computed between the two contour sets. Using the dose distributions from clinical treatment plans, dosimetric differences between the two contour sets were calculated for key indices, including mean dose (D_mean) and maximum dose (D_max). Correlations between geometric metrics and dosimetric differences were analyzed. Dosimetric discrepancy events were defined as Y_3Gy using a threshold of 3 Gy. Within a univariable logistic regression triage framework, each geometric metric was used as an independent variable to estimate the probability of Y_3Gy. Discriminative performance was assessed using receiver operating characteristic (ROC) curves and precision-recall (PR) curves, with the area under the curve (AUC) including ROC-AUC and PR-AUC. Practical operating thresholds were determined via threshold sweeping. Results Geometric similarity metrics showed weak-to-moderate correlations with dosimetric differences; compared with maximum dose, correlations with mean dose differences were more consistent. Among the evaluated metrics, HD95 achieved the best classification performance for Y_3Gy. Threshold sweeping suggested that an HD95 threshold in the range of 4-9 mm can balance the risk of missed discrepancies with the efficiency of automated contour quality assurance (QA). Conclusion Several geometric similarity metrics demonstrated only weak-to-moderate associations with dosimetric differences. For head-and-neck OAR contour QA in this study, HD95 provided the best discriminative performance and can support rapid triage of automated contours, with a practical operating threshold range of 4-9 mm, potentially improving the efficiency of adaptive radiotherapy workflows.

Background Staff in tertiary hospitals are a high-risk group for occupational burnout. Timely identification and precise intervention are crucial for improving healthcare service quality. However, comparative studies on perceived stress and occupational burnout among hospital staff in different positions are lacking. Objective To describe the status of perceived stress and occupational burnout among hospital staff in different positions and compare the differences, explore the relationship between perceived stress and occupational burnout, and identify the influencing factors of occupational burnout. Methods In May 2022, 1526 hospital staff members were selected by convenience sampling from six tertiary hospitals in Guangzhou. Data were collected using a general information questionnaire, the Chinese Perceived Stress Scale (CPSS), and the Maslach Burnout Inventory-Human Services Survey (MBI-HSS) to assess demographic characteristics, perceived stress, and occupational burnout. Chi-square test, one-way ANOVA, and Kruskal-Wallis H test were used to compare differences in perceived stress and occupational burnout among staff in different positions. Pearson correlation analysis was used to explore possible correlation between perceived stress and occupational burnout. Multiple linear regression was conducted to identify influencing factors of occupational burnout. Results A total of 1388 valid questionnaires were collected, with an effective response rate of 90.96%. Among them, 437 nurses (57.0%), 273 physicians (58.6%), 24 pharmacists (57.1%), and 62 administrators (54.9%) reported positive perceived health risk stress (P=0.892). Additionally, 392 nurses (51.1%), 213 physicians (45.7%), 19 pharmacists (45.2%), and 53 administrators (46.9%) reported positive occupational burnout (P=0.005). Perceived stress was positively correlated with occupational burnout among nurses, physicians, pharmacists, and administrators (r=0.5973, 0.5687, 0.5464, and 0.5276, respectively; P＜0.01). The multiple linear regression identified perceived stress (b=1.4587, P＜0.001), job satisfaction (b=−7.4195, P＜0.001), self-rated health (b=−2.0428, P＜0.001), working years (b=−0.1135, P=0.016), and being a nurse (compared with an administrators) (b=3.2435, P=0.045) as significant factors for occupational burnout (P<0.001). Conclusion Perceived health risk stress and occupational burnout are common among hospital staff, with nurses experiencing significantly higher levels of occupational burnout compared to other positions. Hospital managers should pay more attention to staff with higher perceived stress, lower job satisfaction, poorer self-rated health, and shorter working years, particularly nurses.

ObjectiveTo explore the construction of a machine learning model for the diagnosis of traditional Chinese medicine （TCM） syndromes in chronic cough and the optimization of this model using the Voting ensemble algorithm. MethodsA retrospective analysis was conducted using clinical data from 921 patients with chronic cough treated at the Respiratory Department of Dongfang Hospital， Beijing University of Chinese Medicine. After standardized processing， 84 clinical features were extracted to determine TCM syndrome types. A specialized dataset for TCM syndrome diagnosis in chronic cough was formed by selecting syndrome types with more than 50 cases. The synthetic minority over-sampling technique （SMOTE） was employed to balance the dataset. Four base models， logistic regression （LR）， decision tree （dt）， multilayer perceptron （MLP）， and Bagging， were constructed and integrated using a hard voting strategy to form a Voting ensemble model. Model performance was evaluated using accuracy， recall， precision， F1-score， receiver operating characteristic （ROC） curve， area under the curve （AUC）， and confusion matrix. ResultsAmong the 921 cases， six syndrome types had over 50 cases each， phlegm-heat obstructing the lung （294 cases）， wind pathogen latent in the lung （103 cases）， cold-phlegm obstructing the lung （102 cases）， damp-heat stagnating in the lung （64 cases）， lung yang deficiency （54 cases）， and phlegm-damp obstructing the lung （53 cases）， yielding a total of 670 cases in the specialized dataset. High-frequency symptoms among these patients included cough， expectoration， odor-induced cough， throat itchiness， itch-induced cough， and cough triggered by cold wind. Among the four base models， the MLP model showed the best diagnostic performance （test accuracy： 0.9104； AUC： 0.9828）. Compared with the base models， the Voting ensemble model achieved superior performance with an accuracy of 0.9289 on the training set and 0.9253 on the test set， showing a minimal overfitting gap of 0.0036. It also achieved the highest AUC （0.9836） in the test set， outperforming all base models. The model exhi-bited especially strong diagnostic performance for damp-heat stagnating in the lung （AUC： 0.9984） and wind pathogen latent in the lung （AUC： 0.9970）. ConclusionThe Voting ensemble algorithm effectively integrates the strengths of multiple machine learning models， resulting in an optimized diagnostic model for TCM syndromes in chronic cough with high accuracy and enhanced generalization ability.

OBJECTIVE To observe the clinical efficacy of Ziyu Prescription in the treatment of polycystic ovary syndrome relat-ed infertility of kidney deficiency and its effect on endometrial receptivity.METHODS A total of 120 patients with polycystic ovary syndrome related infertility of kidney deficiency were enrolled and randomly divided into a control group and a treatment group,with 60 cases in each group.After excluding dropout cases and excluded cases,56 cases were completed in each group.The control group was treated with letrozole and placebo,while the treatment group was treated with letrozole and Ziyu Prescription.The treatment course of both groups was 3 menstrual cycles.The clinical pregnancy rate and ultrasound Salle score during the implantation window were the main outcome indicators.The traditional Chinese medicine(TCM)syndrome score and serum estradial(E2),testosterone(T),anti-Müllerian hormone(AMH),luteinizing hormone(LH),follicle-stimulating hormone(FSH),homeostatic model assessment of insulin resistance(HOMA-IR),serum E2 and progesterone(P)levels during the implantation window were used as secondary outcome indica-tors.Multivariate Logistic regression and ROC curve analysis were used to evaluate the effect and predictive ability of ultrasound Salle score on pregnancy outcome.Multiple linear regression was used to evaluate the effect of estrogen and progesterone levels during the implantation window on ultrasound Salle score.RESULTS After treatment,the clinical pregnancy rate and ultrasound Salle score during the implantation window in the treatment group were significantly higher than those in the control group(P＜0.05,P＜0.01).The total TCM syndrome score of the treatment group was significantly lower than that of the control group(P＜0.01);the ser-um T,LH/FSH,and HOMA-IR levels of the treatment group were significantly lower than those of the control group(P＜0.05,P＜0.01);the serum E2 and P levels of the treatment group were significantly higher than those of the control group during the implanta-tion window period(P＜0.01).Multivariate Logistic regression analysis and ROC curve analysis showed that ultrasound Salle score was significantly positively correlated with pregnancy rate and had a high predictive value for pregnancy outcome.Multiple linear regression analysis found that there was a linear relationship between the ultrasound Salle score and E2 and P during the implantation window peri-od,and the P level had a greater impact.CONCLUSION Ziyu Prescription can improve the clinical pregnancy rate of patients with polycystic ovary syndrome related infertility of kidney deficiency,effectively improve their endometrial receptivity,regulate the levels of sex hormones and glucose metabolism,reduce the TCM syndrome score,and improve the reproductive quality of patients.

Objective:To investigate lipid metabolism gene mutations and pathogenicity of hypertriglyceridemia acute pancreatitis (HTG-AP) patients.Methods:Clinical data of 495 HTG-AP patients admitted from June 2018 to June 2020 in the center for severe acute pancreatitis of Eastern Theater General Hospital were retrospectively analyzed. Whole-exome sequencing and mutation verification were performed by next-generation sequencing technology and Sanger sequencing. The pathogenicity of gene mutation was analyzed by population mutation ratio, pathogenicity prediction software, conservation scoring software, protein structure prediction, and in vitro experiments. Results:The mutation ratio of lipid metabolism-related genes, namely LPL, APOA5, LMF1, GPIHBP1, and APOC2, were 14.81%, 55.78%, 43.61%, 1.62%, and 0.61%, respectively. Among them, 44 heterozygous mutations in LPL gene were detected including 36 missense mutations, 5 nonsense mutations and 3 frameshift mutations, which were all rarely carried in single patient. Six HTG-AP patients carried the LPL gene heterozygous mutation c.835C>G (p.Leu279Val). The mean level of serum triglyceride at the onset of HTG-AP was 27.4 mmol/L. All of them had a history of recurrent HTG-AP, and most of them had severe acute pancreatitis. The serum LPL concentration and activity were lower than the normal level. The pathogenicity analysis results suggested that the LPL p.Leu279Val was a rare, highly possible pathogenic and highly conserved gene mutation. The in vitro results showed that the LPL p.Leu279Val could significantly reduce the synthesis and secretion ability of LPL as well as its enzymatic activity. Conclusions:The mutation ratio of lipid metabolism-related genes, including LPL, APOA5, LMF1, GPIHBP1, and APOC2, are relatively high in the HTG-AP patients. The LPL p.Leu279Val is a rare and highly possible pathogenic gene mutation, which may lead to recurrent episodes of HTG-AP.

Objectives:To investigate the mechanism of insulin-like growth factor 1(IGF-1)in mediating intervertebral disc cell aging through regulating the activity of the silent information regulator sirtuin 1(SIRT1).Methods:The intervertebral disc tissues of eight-week old female Spargue Dawley(SD)rats were dissected,from which annulus fibrosus(AF)cells were isolated and cultured in vitro.The effects of IGF-1 on the aging of AF cells were studied by β-galactosidase(SA-β-Gal)staining,cell proliferation assays,and cell migration experiments,in exposure to different concentrations of recombinant rat IGF-1(0,1,10,50,100ng/mL)in culture.The molecular mechanism of IGF-1 on AF cells was further explored by Western blot analysis.Results:After treatment with exogenous IGF-1 at different concentrations,SA-β-Gal staining and western blot results showed that IGF-1 promoted AF cell aging in a concentration-dependent manner.The CCK-8 analysis showed that low concentrations of IGF-1 significantly induced AF cell proliferation.Transwell assay results showed that low concentrations of IGF-1 profoundly enhanced the migratory capability of AF cells,whereas IGF-1 at high concentrations demonstrated no significant effect on AF cell migration.Western blot results showed that IGF-1 could downregulate the expression of SIRT1 and elevate the acetylation of p53.Conclusions:IGF-1 can promote AF cell aging though inhibiting SIRT1 expression and activity.

OBJECTIVE To observe the clinical efficacy of Ziyu Prescription in the treatment of polycystic ovary syndrome relat-ed infertility of kidney deficiency and its effect on endometrial receptivity.METHODS A total of 120 patients with polycystic ovary syndrome related infertility of kidney deficiency were enrolled and randomly divided into a control group and a treatment group,with 60 cases in each group.After excluding dropout cases and excluded cases,56 cases were completed in each group.The control group was treated with letrozole and placebo,while the treatment group was treated with letrozole and Ziyu Prescription.The treatment course of both groups was 3 menstrual cycles.The clinical pregnancy rate and ultrasound Salle score during the implantation window were the main outcome indicators.The traditional Chinese medicine(TCM)syndrome score and serum estradial(E2),testosterone(T),anti-Müllerian hormone(AMH),luteinizing hormone(LH),follicle-stimulating hormone(FSH),homeostatic model assessment of insulin resistance(HOMA-IR),serum E2 and progesterone(P)levels during the implantation window were used as secondary outcome indica-tors.Multivariate Logistic regression and ROC curve analysis were used to evaluate the effect and predictive ability of ultrasound Salle score on pregnancy outcome.Multiple linear regression was used to evaluate the effect of estrogen and progesterone levels during the implantation window on ultrasound Salle score.RESULTS After treatment,the clinical pregnancy rate and ultrasound Salle score during the implantation window in the treatment group were significantly higher than those in the control group(P＜0.05,P＜0.01).The total TCM syndrome score of the treatment group was significantly lower than that of the control group(P＜0.01);the ser-um T,LH/FSH,and HOMA-IR levels of the treatment group were significantly lower than those of the control group(P＜0.05,P＜0.01);the serum E2 and P levels of the treatment group were significantly higher than those of the control group during the implanta-tion window period(P＜0.01).Multivariate Logistic regression analysis and ROC curve analysis showed that ultrasound Salle score was significantly positively correlated with pregnancy rate and had a high predictive value for pregnancy outcome.Multiple linear regression analysis found that there was a linear relationship between the ultrasound Salle score and E2 and P during the implantation window peri-od,and the P level had a greater impact.CONCLUSION Ziyu Prescription can improve the clinical pregnancy rate of patients with polycystic ovary syndrome related infertility of kidney deficiency,effectively improve their endometrial receptivity,regulate the levels of sex hormones and glucose metabolism,reduce the TCM syndrome score,and improve the reproductive quality of patients.