Traditional Chinese medicine （TCM）， through its multi-target and systematic regulatory effects， has demonstrated unique advantages in the treatment of gastric precancerous lesions （GPL）. At present， TCM theoretical research on GPL is mainly reflected in three aspects， the integration of macroscopic syndrome differentiation， the inflammation-carcinoma transformation mechanism， as well as the systematization and scientization of theoretical inheritance from famous TCM practitioners. High-quality evidence-based research findings serve as the foundation for clinical practice guidelines on GPL， and TCM has gained international academic recognition in the field of GPL prevention and treatment. Research on TCM mechanisms has yielded a series of important outcomes in the aspects of signaling pathways， gene expression regulation， cellular epigenetics， histone modification， and intestinal microecology. It is proposed that future research on GPL should focus on four key directions， establishing multi-omics data， exploring targeted intervention strategies on key regulatory nodes， advancing the standardization process of integrated traditional Chinese and western medicine prevention and treatment technologies， and constructing stratified screening and intervention platforms. The in-depth integration of TCM microcosmic mechanism of action with its macroscopic syndrome differentiation and treatment system， coupled with interdisciplinary research， will provide valuable references for the clinical treatment and scientific research of GPL.

ObjectiveTo systematically assess the public health governance capacity in Zhejiang Province, to conduct an in-depth analysis of its strengths and weaknesses, so as to provide scientific basis and strategic recommendations for further enhancement. MethodsA systematic collection of policy documents, public information reports, and research literature related to public health governance capacity in Zhejiang Province from 2002 to 2023 was conducted (encompassing a total of 1 263 policy documents, 138 pieces of information reports and 631 research articles). Based on the evaluation criteria suitable for public health systems previously developed by the research team, the basic status and magnitude of change in public health governance capacity in Zhejiang Province was evaluated. Additionally, normative gap analyses were employed to identify the strengths and weaknesses. ResultsZhejiang Province ranked 4th nationwide in terms of public health governance capacity with a score of 733.4 points (1 000.0-point maximum). The province has effectively implemented the principle of health first (scoring 698.5 points in the assessment of health-first strategy implementation) and attached sufficient importance to health-related goals (scoring 658.2 points in the scientific rationality of goal setting). However, the implementation of inter-departmental coordination and incentive mechanisms only scored 178.7 points, the feasibility of management and monitoring mechanisms scored even lower at only 144.0 points, and the coverage of incentive mechanisms scored 286.0 points. ConclusionZhejiang Province has effectively implemented its health first strategy and attached great importance to health targets, but still needs to strengthen cross-departmental coordination mechanisms and health-oriented incentives.

Objective: This study proposes a clustering framework for Chinese materia medica (CMM) based on a large language model (LLM), aiming to explore potential compatibility patterns among CMMs from the semantic perspective of CMM property theory. Methods: First, a CMM property knowledge base was constructed based on Chinese Materia Medica, including 567 commonly used CMMs characterized by four properties, five flavors, and meridian tropism. Then, 49 CMMs derived from 10 prescriptions for Zangdu (脏毒, pathogenic toxins) recorded in Waike Zhengzong (《外科正宗》, Orthodox Manual of External Medicine) and Yangke Xinde Ji (《疡科心得集》, Collected Insights on Ulcer Medicine) were selected as the experimental dataset. Five semantic representation methods—One-Hot, Word2Vec, Bidirectional Encoder Representations from Transformers (BERT), Beijing Academy of Artificial Intelligence General Embedding (BGE), and Qwen—were applied to encode CMM property information into vector representations. Subsequently, t-distributed Stochastic Neighbor Embedding (t-SNE) was used for nonlinear dimensionality reduction on high-dimensional semantic vectors, followed by k-means clustering (k = 7). Clustering performance was evaluated using the Silhouette Score (SS), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). Results: The Qwen-based clustering method, CMM-EmbedCluster, achieved the highest SS (0.607 4) and CHI (158.057 2), as well as the lowest DBI (0.499 5), indicating improved cluster separation and compactness compared with other methods. Visualization of CMM clustering results showed that the clusters were well separated in the low-dimensional space, with strong inter-cluster discrimination and high intra-cluster functional consistency. Further interpretability analysis of CMM clustering results revealed stable structural differences among clusters in terms of four properties, five flavors, and meridian tropism, forming functional partitions consistent with CMM property theory. Conclusion CMM-EmbedCluster utilizes an LLM to achieve semantic-level representation and clustering of CMMs within the framework of CMM property theory, providing support for exploring potential compatibility patterns among CMMs from the perspective of CMM property semantics.

BackgroundIndividuals with chronic multimorbidity exhibit a high prevalence of sleep disturbances， which impair their physical and psychological well-being and impede chronic disease management. Although sleep quality is closely influencing with psychological functioning， empirical evidence regarding the longitudinal trajectories of sleep quality and their influencing factors in this population remains insufficient. ObjectiveTo investigate the longitudinal trajectories of sleep quality and associated factors among patients with chronic multimorbidity from hospitalization through 6 months post-discharge， aiming to facilitate the early identification of high-risk individuals and the development of personalized sleep interventions. MethodsA total of 228 inpatients with chronic multimorbidity， primarily with cardiovascular and metabolic chronic conditions， were enrolled via random sampling at The Third People's Hospital of Fuyang from January 2023 to September 2024. Baseline assessments were conducted during hospitalization using the general socio-demographic questionnaire， the Sleep Dysfunction Rating Scale （SDRS）， the Ruminative Response Scale （RRS）， and the Hospital Anxiety and Depression Scale （HADS）. Sleep quality was further evaluated using the SDRS at 1-， 3-， and 6-month post-discharge. Latent class growth model was employed to delineate trajectories of sleep quality. Multivariate logistic regression analysis was subsequently performed to identify influencing factors of sleep quality. ResultsA total of 201 patients with chronic diseases were included.Based on four predefined assessment time points （baseline， 1 month， 3 months， 6 months post-discharge）， the latent class growth model identified three distinct trajectories of sleep quality： persistent high sleep disturbance （n=72， 35.82%）， moderate sleep disturbance-progression （n=73， 36.32%）， and low sleep disturbance （n=56， 27.86%）. Multivariate logistic regression analysis revealed that， compared with the low sleep disturbance group， multiple factors increased the odds of membership in the persistent high sleep disturbance group： per capita monthly household income of <3 000 yuan （β=13.131， P<0.01） or 3 000–5 000 yuan （β=5.913， P<0.05）， aged 45–65 years （β=9.536， P<0.05）， presence of 3 （β=7.792， P<0.05） or >3 comorbid chronic conditions （β=6.626， P<0.05）， as well as higher RRS （β=0.334， P<0.01） and HADS score （β=1.628， P<0.01）. Additionally， patients aged 45–65 years （β=2.777， P<0.05）， with 3 （β=3.802， P<0.01） or >3 comorbid chronic conditions （β=2.463， P<0.01）， and with higher RRS （β=0.111， P<0.01） and HDAS scores （β=0.350， P<0.05） also demonstrated significantly increased odds of belonging to the moderate sleep disturbance–progression group. ConclusionThere is significant population heterogeneity in sleep quality trajectories among patients with chronic multimorbidity， with age， per capita household monthly income， number of comorbidities， rumination， and anxiety/depression symptoms being identify as the primary influencing factors.

Medical simulation-based assessments have emerged as a vital method for evaluating the clinical competence of healthcare professionals. These assessments provide examinees with opportunities to practice clinical skills in a controlled environment that mirrors real-life medical scenarios. However, assessment organizers must implement specific measures to ensure that these assessments yield valid and reliable results. The reliability of simulation-based assessments is a crucial component of quality assurance. This article delineates twelve practical recommendations to enhance the reliability of medical simulation-based assessments across the three key dimensions of raters, simulation environment, and assessment tools. These recommendations include ① selecting raters with relevant professional backgrounds and extensive experience in simulation-based teaching and assessment; ②providing raters with standardized training for the assessment; ③conducting mock ratings and piloting to reach consensus among raters; ④preventing excessive work hours or cognitively overload while rating; ⑤providing standardized personnel with adequate training and re-training; ⑥implementing immediate and long-term quality assurance measures for standardized patient role performance; ⑦maintaining consistent operation of simulation equipment; ⑧guaranteeing the simulation provides adequate functional fidelity for the examinees; ⑨clearly defining each element and item in assessment tools while controlling their quantity; ⑩ensuring consistency when multiple assessment tools are used; 11using assessment tools with evidence of construct validity when possible; and 12establishing systems to ensure consistency in data (scoring) collection while safeguarding data security. These strategies aim to assist medical educators in developing structured and reliable simulation-based assessment schemes.

Objective:To evaluate the feasibility and outcomes of transnasal endoscopic clipping of the paraclival internal carotid artery (ICA) in skull base surgery.Methods:The paraclival ICA was anatomically dissected in cadaveric head specimens. The clinical data of 15 patients with skull base lesions involving the ICA who admitted to the Department of Otorhinolaryngology Head and Neck Surgery at Tianjin Huanhu Hospital from January 2021 to December 2023 were retrospectively analyzed. Among them, 4 patients underwent transnasal endoscopic clipping of the paraclival ICA and concurrent lesion resection. The surgical methods were summarized, and the key points and indications of this technique were analyzed.Results:Intraoperative clipping of the ICA was successful and hemostatic in all 4 patients. Postoperatively, 3 patients had no complications, while 1 patient developed delayed ischemic cerebral infarction. Two patients were cured, 1 patient was maintained on immunotherapy, and 1 patient died. During follow-up, the clip was in situ in 1 patient, had detached in another, and was obscured by temporal muscle coverage in the remaining 2 patients.Conclusions:Transnasal endoscopic clipping of the paraclival ICA represents a potential option for managing the ICA in skull base surgery. However, it carries significant risks and limitations, mandating careful patient selection based on specific circumstances.

The efficacy of preoperative neoadjuvant chemoradiotherapy(nCRT)in locally advanced rectal cancer(LARC)is predicted using radiomic features of the target areas in radiotherapy for rectal cancer and an interpretable machine learning model.The clinical data are collected from 290 LARC patients who are divided into effective and ineffective groups based on tumor regression grade.The extracted radiomic features and clinicopathological data are used to develop prediction models.The optimal model is determined based on AUC performance evaluation,and the explanatory analysis is conducted using nomogram and decision curve.A total of 223 patients are included in the study,with 48 in the effective group.There are 156 patients in the training set(34 in the effective group)and 67 patients in the validation set(14 in the effective group).The nomogram model shows the best performance,with AUC of 0.858 in the training set and 0.844 in internal test set,and decision curve analysis demonstrated its superior net clinical benefit across most threshold ranges than other models.Combining radiomics and clinical variables,the nomogram can effectively predict nCRT outcomes and support clinical decision-making.

The efficacy of preoperative neoadjuvant chemoradiotherapy(nCRT)in locally advanced rectal cancer(LARC)is predicted using radiomic features of the target areas in radiotherapy for rectal cancer and an interpretable machine learning model.The clinical data are collected from 290 LARC patients who are divided into effective and ineffective groups based on tumor regression grade.The extracted radiomic features and clinicopathological data are used to develop prediction models.The optimal model is determined based on AUC performance evaluation,and the explanatory analysis is conducted using nomogram and decision curve.A total of 223 patients are included in the study,with 48 in the effective group.There are 156 patients in the training set(34 in the effective group)and 67 patients in the validation set(14 in the effective group).The nomogram model shows the best performance,with AUC of 0.858 in the training set and 0.844 in internal test set,and decision curve analysis demonstrated its superior net clinical benefit across most threshold ranges than other models.Combining radiomics and clinical variables,the nomogram can effectively predict nCRT outcomes and support clinical decision-making.

Objective:To investigate the epidemic characteristics of severe fever with thrombocytopenia syndrome (SFTS) in Tiantai County Zhejiang Province, and provide a scientific basis for prevention and control of SFTS.Methods:The case data of SFTS in Tiantai County from 2014 to 2024 were collected through the Chinese Disease Control and Prevention Information System, and descriptive epidemic method was employed to analyze its epidemic and clinical characteristics.Results:From 2014 to 2024, a total of 114 SFTS cases were reported in Tiantai County, with an average annual incidence rate of 2.21/100 000. The peak incidence occurred in 2021 (3.40/100 000, 16 cases). Nine cases died, with a mortality rate of 7.89% (9/114). The male-to-female ratio was 1.00∶0.97 (58∶56). The high-risk population was middle-aged and elderly people aged over 50 years old, accounting for 91.23% (104/114). The occupation was mainly farmers, accounting for 92.98% (106/114). The incidence of SFTS had obvious seasonality, with a peak from April to September, accounting for 83.33% (95/114). The average onset date was calculated to be June 25th, and the peak period was from April 19th to August 31st. The distribution of cases was mainly in Tantou Town (34 cases), Shiliang Town (23 cases), Yongxi Township (17 cases), and Pingqiao Town (12 cases), accounting for 75.44% (86/114). From 2014 to 2024, the affected areas had gradually expanded from 1 township/subdistrict to 13 townships/subdistricts, showed a phenomenon of migration from east to west. Among the 114 patients with SFTS, the initial symptom was fever. The proportion of neuropsychiatric symptoms, atrial fibrillation and heart failure in the death group was significantly higher than those in the survival group (χ 2 = 19.24, 16.44, 23.90, P < 0.001). Conclusion:From 2014 to 2024, the incidence of SFTS in Tiantai County fluctuates with obvious seasonal and regional migration characteristics.

Objective To explore the molecular mechanisms of moxibustion in improving osteoporosis in rats using high-throughput micro-RNA(miRNA)sequencing analysis.Methods A total of 18 female Sprague-Dawley(SD)rats were randomly divided into an operation group(12 rats),which was subjected to ovariectomy to induce osteoporosis,and a sham operation control(SO)group(6 rats).The 12 osteoporosis model rats were randomly divided into a model(OVX)group and a moxibustion(MOX)group(6 rats per group),which were treated with once-daily moxibustion at the"Shenshu"(BL23)and"Guanyuan"(CV4)acupoints for 20 minutes each time,for 12 weeks.Micro-computed tomography(CT)scans of the rat femur were taken to analyze the trabecular bone thickness(Tb.Th),trabecular separation(Tb.Sp),and bone volume total volume(BV/TV)ratio of the trabecular bone.Hematoxylin and eosin staining was used for morphological observation of tibial tissues.Serum alkaline phosphatase(ALP)and osteocalcin(OCN)levels were measured using ELISA.Three randomly selected rats from each group were used for miRNA high-throughput sequencing to screen for differentially expressed miRNAs,which were subjected to functional enrichment analysis and target gene prediction.Results Micro-CT images showed that,compared with the OVX group,the MOX group had superior bone density,significant increases in the Tb.Th and BV/TV score,and a significant decrease in Tb.Sp(P＜0.05).ELISA indicated that,compared with the OVX group,the MOX group showed a significant decrease in serum ALP activity and a significant increase in serum OCN content(P＜0.05).The miRNA sequencing result showed that 34 miRNAs were commonly expressed between the OVX group and the intervention group,and 15 miRNAs were commonly expressed between the OVX group and the intervention group.KEGG pathway enrichment analysis showed that the differentially expressed genes were mainly enriched in signaling pathways involving MAPK,Ras,FoxO,TNF,and cancer-related microRNAs.For the top five most differentially expressed microRNAs,namely miR-153-5p,miR-201-5p,miR-449c-5p,miR-451-3p,and miR-153-3p,target gene predictions yielded 10 major targets:Ebf2,Rtn4,Fbxl3,Naa15,Vamp2,Daam1,Akap6,Camta1,Ptprz1,and Lamp1.Conclusions Acupuncture slowed the progression of osteoporosis,improved bone microstructure,and balanced bone metabolism in rats.This therapeutic effect may be achieved through regulation of the expression of microRNAs and their target genes;however,the underlying mechanism requires further exploration.