As the core vehicle for preserving and transmitting traditional Chinese medicine（TCM） academic thought and clinical experience， the establishment of a robust quality evaluation system for TCM clinical case reports is a crucial component in the current standardization and modernization of TCM. Based on the practical experience of constructing the China Clinical Cases Library of Traditional Chinese Medicine by the China Association of Chinese Medicine， this study conducted a comprehensive analysis of critical challenges， including insufficient authenticity and unfocused evaluation criteria. It proposed a three-dimensional evaluation framework grounded in the structure-process-outcome logic， encompassing three dimensions of authenticity and standardization， characteristics and advantages， application and translational impact. This framework integrated 12 key evaluation indicators in a systematic manner. The model preserved the academic characteristics of TCM syndrome differentiation and treatment， while aligning with modern scientific research standards， achieving a balance between individualized TCM experience and standardized evaluation. Concurrently， this study provided theoretical foundations and methodological guidance for evaluating the quality of TCM clinical cases， contributing significantly to the inheritance of TCM knowledge， evidence-based practice， and the reform of talent evaluation mechanisms.

Objective: To understand the current status and related factors of oral health behavior in preschool children, so as to provide scientific basis for improving parents oral health care abilities. Methods: From March 5 to 12, 2024, using a stratified cluster sampling method, an online questionnaire survey was conducted among parents of 1 381 preschool children aged 3-6 from 6 selected kindergartens of a district in Beijing. A structural equation model (SEM) was constructed based on the Theory of Planned Behavior framework to analyze the factors associated with preschool children s oral health behaviors. Results: The rates of parental assistance in brushing teeth, regular oral examination, and postprandial rinsing were 44.4%, 18.6% and 23.6%, respectively. Attitude, subjective norms were positively correlated with oral health behavior intention ( r =0.61, 0.55); attitude, perceived behavioral control, and behavioral intention were positively correlated with oral health behavior ( r =0.14, 0.13, 0.16) (all P < 0.01 ). The results of the SEM showed that attitude and subjective norms had a significant direct effect on oral health behavior intention ( β = 0.42 , 0.40), and the direct effect of perceived behavioral control on oral health behavior was statistically significant ( β =0.42) (all P <0.01). Conclusions The overall level of oral health behavior among preschool children needs to be improved. Perceived behavioral control is the most direct positive related factor of oral health behavior. In the future, intervention measures should focus on perceived behavioral control as the entry point and provide systematic intervention guidance in combination with planned behavior theory.

Objective To control the stepwise release of vascular endothelial growth factor A(VEGF-A)within the microcapsules,and to analyze the effects of the microcapsules on cellular angiogenic capability.Methods VEGF-A encapsulated poly(lactic-co-gly-colic acid)(PLGA)microcapsules were prepared using a method combining dual-channel coaxial injection and continuous flow technol-ogy.The release and degradation performance of the microcapsules were characterized using a phosphate-buffered saline(PBS)soaking method.The biocompatibility of the microcapsules was assessed through the CCK-8 method and Calcein-AM/PI staining method.The impact of microcapsule extract on cellular angiogenesis ability was examined by conducting cell scratch assays and tubule formation ex-periments.Results The microcapsules were round in shape,with their particle diameter measuring in the range of hundreds of mi-crometers.Microcapsules with a molecular weight(Mw)-12 ku can release a large amount of VEGF-A in the initial phase,while Mw-30 ku ones had the capacity to provide a stable,long-term,low-dose release of VEGF-A.Microcapsules of Mw-12 ku exhibited outstanding potential for enhancing the healing of cell scratch wounds in the initial phase.Moreover,within the 0-12 day period,the two types of microcapsule extracts significantly enhanced the ability of cells to form tubules in vitro.Conclusion This study successfully regulated the release profile of VEGF-A by adjusting the molecular weight of PLGA,achieving an initial rapid and substantial release of VEGF-A followed by a sustained slow release over time,while maintaining its biological activity throughout the process.

This study investigated the drug resistance and genetic relationships among strains co-existing in animals,the environ-ment,and the living quarters of employees at large-scale pig farms in certain regions of Shandong Province,to provide a scientific ba-sis for elucidating the transmission mechanisms of drug-resistant bacteria through bacterial traceability analysis.Samples were col-lected from two pig farms,and bacteria were isolated and purified.The species of the isolated strains were identified via 16S rRNA gene sequencing.Antimicrobial susceptibility testing was conducted with a VITEK-2 Compact system and the disk diffusion method for strains present in pigs,the environment,and living areas.Furthermore,whole-genome sequencing was performed on the Illumina Miniseq platform to annotate drug resistance genes,and multilocus sequence typing(MLST)and core genome single nucleotide poly-morphism(cgSNP)analyses were used to trace the resistant strains.Three species—Staphylococcus aureus,Pseudomonas aeruginosa,and Bacillus cereus—were isolated and cultured from animals,the environment,and employee living areas,and their distributions were analyzed.These strains exhibited diverse drug resistance spectra and genetic diversity.Additionally,the strains displayed highly consistent resistance profiles,resistance genes,ST types,and SNP loci in pig urine,soil both inside and outside the facility,human drinking water,and the cafeteria and dormitories.Our findings indicated a potential risk of transmission of opportunistic pathogens be-tween the pig farming area and the living quarters.Particular attention should be paid to the environmental transmission of methicillin-resistant Staphylococcus aureus.

Objective:To understand the status and influencing factors of social isolation in elderly individuals with chronic diseases.Methods:A multi-stage sampling method was used to select elderly individuals from eight communities or villages in Hefei from July to September 2022. The study employed a general information survey, the Lubben Social Network Scale (LSNS-6) , the Geriatric Depression Scale (GDS-15) , the Social Support Rating Scale (SSRS) , and the Personal Social Capital Scale 16 (PSCS-16) . Binary Logistic regression analysis was used to explore the factors influencing social isolation in elderly individuals with chronic diseases.Results:A total of 1 133 elderly individuals were surveyed, among which 538 had chronic diseases. Among the 538 elderly individuals with chronic diseases, 209 were socially isolated, resulting in a social isolation rate of 38.8%. Binary Logistic regression analysis revealed that living area, fear of falling, depression, social capital, and social support were significant factors influencing social isolation ( P<0.05) . Conclusions:The social isolation rate among elderly individuals with chronic diseases is high. Special attention should be given to elderly individuals living in rural areas, those who fear falling, and those experiencing depression. Additionally, improving social capital and social support can help alleviate social isolation in these elderly individuals.

Objective To develop and validate a machine learning-based predictive model to assess the in-hospital mortality risk of patients with myocardial infarction(MI)complicated by heart failure(HF)undergoing percutaneous coronary intervention(PCI).Methods This retrospective study analyzed MI patients with HF who underwent PCI at The First Affiliated Hospital of Xinjiang Medical University from January 2019 to January 2023.Patient data,including demographic characteristics,vital signs,laboratory test results,imaging parameters and medication use,were collected and randomly divided into a training set(70％)and a validation set(30％).The extreme gradient boosting(XGBoost)model was used to identify variables significantly associated with in-hospital mortality,and the Shapley additive explanations(SHAP)model was applied to assess feature importance.A predictive model was then constructed using univariate and multivariate Logistic regression analyses.Model performance was evaluated using receiver operating characteristic(ROC)curves,area under the curve(AUC)values,calibration curves,and decision curve analysis.Finally,a nomogram was developed for intuitive risk assessment.Results A total of 1 214 MI patients with HF were included in the study,with a median age of 64 years.The in-hospital mortality rate was 7.41％(90 deaths).XGBoost feature selection identified ten key predictive variables:age,myoglobin,albumin,fasting blood glucose,N-terminal pro-B-type natriuretic peptide(NT-proBNP),diabetes mellitus,creatinine,cystatin C,procalcitonin,and left ventricular ejection fraction.Based on these variables,a Logistic regression model was developed,with seven final predictors:age,diabetes mellitus,creatinine,fasting blood glucose,cystatin C,NT-proBNP,and albumin.The model demonstrated high predictive accuracy,with AUC value of 0.869(95％CI:0.84-0.89)in the training set and 0.827(95％CI:0.79-0.85)in the validation set.The calibration curve indicated that the predicted probabilities were consistent with the actual observed outcomes,and decision curve analysis showed that the model had a high net benefit across various decision thresholds.Conclusion This study developed a machine learning-based predictive model incorporating Logistic regression to assess the in-hospital mortality risk of MI patients with HF undergoing PCI.The model demonstrated high predictive performance and clinical utility.The nomogram derived from this model provides an intuitive tool for individualized risk assessment,aiding clinicians in the early identification of high-risk patients,optimizing intervention strategies,and improving patient outcomes.

Objective To construct and validate machine learning-based models to predict the risk of acute kidney injury(AKI)following percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 2 315 STEMI patients who underwent PCI between January 2020 and June 2023 were included;306(13.2％)of them developed AKI.Baseline variables were screened using LASSO regression,with the optimal λ value selected via 10-fold cross-validation to identify AKI-associated features.Subsequently,eight distinct machine learning models were constructed and evaluated for their predictive performance.SHAP value analysis was employed to assess the impact of key variables on model predictions.Results LASSO regression identified seven variables significantly associated with AKI,including age,multivessel disease,preoperative creatinine,heart failure,white blood cell count,hemoglobin,and albumin levels.Among all the models,the light gradient boosting machine(LGBM)and extreme gradient boosting(XGB)demonstrated the best predictive performance,with training set AUCs being 0.899(95％CI:0.877-0.921)and 0.893(95％CI:0.868-0.918),and validation set AUCs being 0.809(95％CI:0.763-0.856)and 0.871(95％CI:0.833-0.909),respectively.SHAP analysis revealed that albumin,age,preoperative creatinine,and white blood cell count were the primary contributors to AKI risk.Conclusion This study successfully developed and validated machine learning-based predictive models capable of effectively identifying the risk of AKI following PCI in STEMI patients,thus providing valuable support for clinical decision-making.

In January 2023, a patient with soft tissue degloving defect of right index, middle, ring and little fingers was treated in the Department of Hand Surgery, Suzhou Ruihua Orthopaedic Hospital. Seven free flaps from a leg were harvested to reconstruct the defected wound of fingers in primary surgery. Flap thinning and plastic surgery were performed in stage-II surgery. Over the 22 months of postoperative follow-up, the flaps in right index, middle, ring and little fingers survived well with the colour and texture close to proximal skin. There was no obvious swelling of the flaps and sensation of the flaps recovered to S 3. The donor sites healed well and the donor leg walked normally.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To investigate whether the application of automated software for computed tomography angiography(CTA)and computed tomography perfusion imaging(CTP)can improve in-hospital workflow for endovascular treatment(EVT)in acute ischemic stroke patients.Methods We included patients with acute ischemic stroke who received CTA and CTP evaluation followed by EVT through the stroke emergency pathway at the Affiliated Hospital of Southwest Medical University between January 1,2020 and December 30,2022.The patients were divided into two groups:control group and artificial intelligence(Al)group based on whether automated software was used for assessment.The control group consisted of patients who underwent manual post-processing of multimodal imaging before June 2021,while the AI group was composed of patients whose imaging was processed with automated software from July 2021 onwards.The primary outcome was door-to-puncture time(DPT),and the secondary outcome was the 90-day modified Rankin Scale(mRS)score.Results A total of 312 patients were included,with 145 in the control group and 167 in the AI group.The median age of all the patients was 68 years(range:58-74 years),and 55.4％(173 patients)were male.The median National Institutes of Health Stroke Scale(NIHSS)score at presentation was 16 scores(range:12-19 scores).The median DPT was reduced from 110 min(range:80-150 min)before the use of automated software to 95 min(range:65-125 min)after its implementation(P＜0.001).However,there was no significant difference in the proportion of patients achieving functional independence(mRS score of 0-2)between the two groups(39.3％vs.41.3％,P=0.719).Conclusion The application of multimodal CT automated software improves the in-hospital workflow for acute ischemic stroke patients by reducing the time to EVT.However,the software did not significantly impact neurological functional outcomes as measured by the mRS.