ObjectiveTo develop a community-family management model for older adults with mild cognitive impairment (MCI) and to formulate detailed application specifications, and to fully leverage the initiative of communities and families under limited resource conditions, for achieving community-based early detection and early intervention for older adults with MCI. MethodsA systematic literature review was conducted to identify pertinent publications. Corpus-based research methodologies were employed to extract, refine, integrate and synthesize management elements, thereby establishing the specific content and service processes for each stage of the management model. Utilizing the 5W2H analytical framework, essential elements such as management stakeholders, target populations, content and methods for each stage were delineated. The model and its application guidelines were finalized through expert consultation and demonstration. ResultsAn expert evaluation of the management model yielded mean scores of 4.84, 4.32 and 4.84 for acceptability, feasibility and systematicity, respectively. By integrating the identified core elements with expert ratings and feedback, the final iteration of the community-family management model for older adults with MCI was formulated. This model comprised of five stages: screening and identification, comprehensive assessment, intervention planning, monitoring and referral pathways to ensure implementation, and enhanced support for communities, family members and caregivers. Additionally, it included 18 specific application guidelines. ConclusionThe proposed management model may theoretically help delay cognitive decline, improve cognitive function and potentially promote reversal from MCI to normal cognition. It may also enhance the awareness and coping capacity of older adults and their families, strengthen community healthcare professionals' ability to early identify and manage MCI.

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.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

ObjectiveThis paper aims to explore the risk factors for chronic atrophic gastritis （CAG） with turbidity toxin accumulation syndrome and establish a prediction model. MethodsClinical data of 180 patients with CAG who participated in the "clinical study of Xianglian Huazhuo Particles blocking CAG cancer transformation" of Hebei Sheng Zhong Yi Yuan from July 2021 to March 2022 were collected. After confounding factors were controlled by propensity score matching， patients were divided into a training set （namely dev） and a validation set （namely vad） in a seven to three ratio. The risk factors for CAG with turbidity toxin accumulation syndrome in the training set were investigated by using univariate Logistic regression analysis and least absolute shrinkage and selection operator （namely Lasso） regression algorithms. Subsequently， a model， named model 1se， was developed by using the training set data to predict the risk factors for CAG with turbidity toxin accumulation syndrome. The accuracy of the prediction model was assessed by using various methods， including the receiver operating characteristic （ROC） curve， Hosmer-Lemeshow test （H-L）， calibration plot， and decision curve analysis （DCA）. ResultsAge， body mass index （BMI）， family history of cancer， job and life satisfaction， yellow and greasy fur with slippery pulse， and heavy body sensation were independent risk factors of the model. The prediction model showed excellent predictive value for both the training and validation sets. ConclusionThe established prediction model for CAG with turbidity toxin accumulation syndrome has high discrimination and excellent calibration， which could provide an excellent clinical basis for disease diagnosis and individualized treatment of patients.

Objective To investigate the effect of bone metabolic markers on sarcopenia in elderly patients with type 2 diabetes mellitus (T2DM). Methods A total of 412 patients with T2DM in the department of endocrinology of Nanjing Central Hospital from May 2020 to June 2025 were selected as the research subjects. According to Asian Working Group for Sarcopenia (AWGS) in 2019, these patients were evaluated for skeletal muscle mass index (ASMI), muscle strength, and muscle function, and were divided into a sarcopenia group (84 cases) and a non-sarcopenia group (328 cases). The glucolipid metabolic indexes were detected in both groups of patients, and the bone metabolic markers were evaluated, including procollagen type 1 N-terminal peptide (P1NP), beta-C-terminal telopeptide of type 1 collagen (β-CTX), and 25-hydroxy vitamin D [25-(OH)D]. The factors influencing the occurrence of sarcopenia in T2DM patients were analyzed by logistic regression analysis, and the diagnostic values of bone metabolic markers on sarcopenia in patients with T2DM were assessed by ROC curve. Results The levels of P1NP and 25-(OH)D were lower, while β-CTX level was higher in the sarcopenia group compared to the non-sarcopenia group, with statistical differences (P<0.05). After logistic correlation analysis, it was found that P1NP, β-CTX and 25-(OH)D were all influencing factors for the occurrence of sarcopenia in T2DM patients. ROC curve analysis suggested that combined detection of PINP, β-CTX, and 25-(OH)D had higher diagnostic value, with an area under the curve up to 0.805. Conclusion The abnormal expression of bone metabolic markers is associated with the increased risk of sarcopenia in patients with T2DM. The detection of serum bone metabolic markers expression level is of certain significance for the assessment of diabetes-related sarcopenia.

ObjectiveTo investigate the mechanisms by which Bushen Tongluo Jiangzhuo prescription improves renal fibrosis in rats with chronic kidney disease （CKD） through the phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR） signaling pathway. MethodsSeventy specific pathogen-free （SPF） Sprague-Dawley （SD） rats were randomly divided into a control group （n=15） and a modeling group （n=55）. Rats in the modeling group were administered a 2.5% adenine suspension at a dose of 200 mg·kg^-1·d^-1 by gavage for 4 weeks to establish a CKD model. Successfully modeled rats were randomly divided into a model group， an irbesartan group （20.25 mg·kg^-1·d^-1）， and Bushen Tongluo Jiangzhuo prescription low-， medium-， and high-dose groups （5.82， 11.64， and 23.28 g·kg^-1·d^-1， respectively）， with 10 rats in each group. Each group was administered an equal volume of physiological saline， the corresponding concentration of irbesartan， or Bushen Tongluo Jiangzhuo prescription by gavage for 12 weeks. Body weight and renal function indices were dynamically monitored. Serum creatinine （SCr）， blood urea nitrogen （BUN）， urine albumin-to-creatinine ratio （ACR）， 24-hour urinary total protein （24 hUTP）， aspartate aminotransferase （AST）， alanine aminotransferase （ALT）， interleukin-1β （IL-1β）， interleukin-6 （IL-6）， and tumor necrosis factor-α （TNF-α） levels were measured using an automatic biochemical analyzer. Renal histopathological changes were observed by hematoxylin-eosin （HE） and Masson staining. Immunohistochemistry （IHC） was used to detect the expression of PI3K， Akt， phosphorylated Akt （p-Akt）， and mTOR in renal tissues. Western blot was performed to assess the protein expression of PI3K， p-Akt， Akt， phosphorylated mTOR （p-mTOR）， and mTOR in renal tissues. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to determine the mRNA expression levels of PI3K， Akt， and mTOR in renal tissues. ResultsCompared with the model group， rats in the irbesartan group and the low-， medium-， and high-dose Bushen Tongluo Jiangzhuo prescription groups showed significantly decreased levels of SCr， BUN， ACR， 24 hUTP， IL-1β， IL-6， and TNF-α （P<0.01）. AST levels were significantly increased （P<0.01）， while no significant difference was observed in ALT levels. Histopathological examination revealed that， compared with the model group， renal tubular epithelial cell edema and necrosis and Bowman's capsule dilation were alleviated， inflammatory cell infiltration was reduced， and interstitial and glomerular fibrosis was markedly improved in all treatment groups， with the most pronounced effect observed in the high-dose Bushen Tongluo Jiangzhuo prescription group. Real-time PCR results showed that mRNA expression levels of PI3K， Akt， and mTOR were significantly downregulated in the high-dose group （P<0.01）. IHC results demonstrated that PI3K and p-Akt expression levels in renal tissues were significantly decreased in the high-dose group （P<0.01）. Western blot analysis further confirmed that the expression levels of PI3K， p-Akt/Akt， and p-mTOR/mTOR were significantly reduced in the high-dose group （P<0.01）. ConclusionBushen Tongluo Jiangzhuo prescription improves renal function indices in CKD rats， reduces collagen deposition in renal tissues， and decreases serum inflammatory factor levels. Its protective effect on renal function may be achieved by activating autophagy through downregulation of the PI3K/Akt/mTOR signaling pathway， thereby alleviating renal fibrosis.

ObjectiveTo investigate the value of predictive models established based on machine learning methods in predicting the risk of metabolic associated fatty liver disease （MAFLD）， and to analyze its key risk factors. MethodsA retrospective analysis was performed for the 50 variables of 2 168 healthy individuals who underwent physical examination in Department of Health Assessment， Xiyuan Hospital， China Academy of Chinese Medical Sciences， from January 2021 to December 2024， including body composition， past history， and laboratory tests， and according to whether they were diagnosed with MAFLD or not， they were divided into MAFLD group with 265 individuals and non-MAFLD group with 1 903 individuals. The Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. Randomly split the research data into a training set and a validation set in a 70% to 30% ratio. Predictive factors were screened from the training set data using univariate analysis， LASSO regression， and multivariate Logistic regression analysis. Predictive models were then constructed using seven machine learning methods： Logistic regression， decision tree， random forest （RF）， eXtreme gradient boosting， light gradient boosting machine， support vector machine， and artificial neural network. Model performance was evaluated by plotting receiver operating characteristic curve for the validation set and calculating the area under the curve （AUC）， sensitivity， specificity， and Youden index for each model. Furthermore， the SHapley Additive exPlanation （SHAP） method was used to analyze the contribution of variables in the optimal model. ResultsThe prevalence rate of MAFLD among the 2 168 subjects was 12.22% （265/2 168）. Smoking， diastolic blood pressure， phase angle， visceral fat area， muscle fat ratio， waist-to-hip ratio， aspartate aminotransferase， non-HDL-C/HDL-C ratio， triglyceride-glucose index， and gallstones were independent risk factors for MAFLD （all P<0.05）. The seven predictive models of support vector machine， eXtreme gradient boosting， decision tree， light gradient boosting machine， artificial neural network， RF， and Logistic regression had an AUC of 0.738， 0.754， 0.757， 0.786， 0.795， 0.796， and 0.815， respectively， in the validation set， among which the RF model had the best discriminatory ability （AUC=0.796， 95% confidence interval： 0.754 — 0.839）， with a sensitivity of 81.01%， a specificity of 63.16%， and a Youden index of 44.17%. The SHAP analysis showed that visceral fat area， waist-to-hip ratio， and diastolic blood pressure were the top three predictive factors in terms of importance. ConclusionThe RF model， constructed based on body composition and clinical indicators， has a good performance in predicting the risk of MAFLD， and its interpretability can help to identify high-risk individuals in the early stage in clinical practice.

OBJECTIVE To establish a scientific， systematic， multi-dimensional performance management system for pharmacy practice， so as to improve the efficiency and quality of pharmacy practice performance management in public hospitals. METHODS Based on the four dimensions of the balanced scorecard theory， finance， customer， internal process， learning and growth， reference indicators for pharmacy practice performance management were summarized. The Delphi method was used to screen indicators， and the analytic hierarchy process was applied to determine the weights of indicators. A pharmacy practice performance management system was then constructed. Based on this system， action plans were formulated and implemented. The effectiveness was evaluated from two aspects： customer reviews and changes in pharmacy practice outcomes. RESULTS A total of 28 reference indicators were summarized， and a performance management system for pharmacy practice was constructed， consisting of 4 primary indicators， 9 secondary indicators， and 20 tertiary indicators. Compared with action plans implementation before， the satisfaction of clinical departments was significantly improved， and 11 pharmacy practice performance management indicators were optimized after implementation. CONCLUSIONS A scientific and systematic performance management system for pharmacy practice has been successfully established， which can provide a reference for the innovation of hospital pharmacy practice management and the high quality development of pharmacy practice.

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.

Objective: To investigate the clinical symptoms, laboratory tests, and treatment strategies of a case of fetal/neonatal alloimmune thrombocytopenia (FNAIT) complicated with piperacillin drug antibody. Methods: The platelet antibodies in the mother were screened and identified by ELISA. The HLA antigens of the newborn were genotyped through PCR-SSO, while the specificity of HLA antibodies in the mother was determined using a Single Antigen kit. The drug antibody was detected by a piperacillin kit. Results: Maternal antibodies against paternally-derived platelet antigens were detected. The HLA genotypes of the newborn were identified as HLA A 33∶03 and HLA B 58∶01. The mother exhibited strong positive antibodies against the specific platelet antigens of the newborn, namely anti-HLA-A33 and anti-HLA-B58 antibodies. The piperacillin antibody was detected in the newborn. Following treatment of continuous intravenous immunoglobulin (IVIG), platelet transfusions, red blood cell transfusions and discontinuation of piperacillin treatment, the platelet count and hemoglobin levels increased in the newborn. Conclusion: The newborn in this case was diagnosed with FNAIT complicated by the presence of anti-HLA-A33 and anti-HLA-B58 antibodies, as well as drug-induced hemolytic anemia caused by piperacillin drug antibody. The condition is more complicated under the influence of dual immune antibodies. Laboratory detection techniques such as platelet antibody and drug antibody tests can assist in early clinical diagnosis. At the same time, more active drug and blood transfusion treatments should be given in clinical practice to improve the prognosis.