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.

During habitation on low-Earth-orbit (LEO) space stations, astronauts are exposed to a combination of orbital microgravity, space radiation, an enclosed and artificially controlled cabin environment, altered sleep-wake rhythms, and sustained operational and psychosocial demands. These factors can modify sensorimotor inputs, body-fluid distribution, arousal state, and task-related neural processing, thereby affecting brain function and behavior. Because evidence from real spaceflight remains limited by small samples, heterogeneous mission durations, and different measurement time points, this review integrates findings from real human spaceflight with relevant ground-based analog studies while treating real orbital studies as the primary source of evidence. We focus on electroencephalography (EEG) and event-related potentials (ERPs), structural and functional neuroimaging, and sensorimotor and cognitive outcomes, with particular attention to the direction, anatomical distribution, temporal course, and possible physiological significance of the reported changes. EEG studies indicate that long-duration spaceflight is associated with increased resting-state beta activity, particularly in sensorimotor and frontotemporal regions, together with state-dependent alterations in beta-band functional connectivity. In contrast, alpha source power and functional connectivity associated with the default mode network are reduced during long-duration missions, and some of these effects may persist into the early postflight period. Task-related electrophysiological findings further suggest that sustained mental workload can weaken frontocentral theta-temporoparietal gamma phase-amplitude coupling, whereas in-flight decision-making is accompanied by reduced P300 amplitude and reorganization of feedback-related potentials. Short-duration missions, however, have shown relative preservation of posterior dominant rhythms and some ERP measures, indicating that mission duration, recording state, and task demands substantially influence the observed electrophysiological phenotype. Neuroimaging provides complementary evidence at structural and network levels. Long-duration spaceflight has been associated with upward brain displacement, ventricular and cerebrospinal fluid expansion, regional gray- and white-matter remodeling, and alterations in white-matter structural connectivity. Functional magnetic resonance imaging (fMRI) studies further indicate reorganization of vestibular-sensorimotor, visual, and default mode networks. Some network changes recover after return to Earth, whereas others may remain detectable for months. Importantly, most MRI measurements are obtained after landing rather than in orbit; therefore, postflight findings may reflect a mixture of in-flight adaptation, the rapid transition back to 1 g, and subsequent readaptation rather than a single spaceflight effect. Behavioral evidence is most consistent for sensorimotor function. Early after return, astronauts frequently show impaired postural control, gait, spatial orientation, and coordination, consistent with reweighting among vestibular, visual, and proprioceptive information. By comparison, simple cognition and some critical operational tasks are often preserved. More subtle or high-load processes-including sustained attention, feedback evaluation, temporal processing, error monitoring, and metacognitive calibration may be more sensitive than global cognitive performance. This dissociation between neural change and preserved behavior suggests that altered brain activity should not automatically be interpreted as functional deterioration. Integrating the available modalities, we propose that LEO space-station habitation is better characterized by system-specific and stage-dependent neural adaptation than by a generalized increase or decline in brain function. The most coherent cross-modal pattern involves sensorimotor reorganization: reduced gravitational reference and weight-bearing feedback under microgravity may drive reweighting of vestibular, visual, and proprioceptive inputs, accompanied by changes in beta activity and vestibular-sensorimotor networks and followed by transient postflight sensorimotor deficits. A second major process involves fluid-related structural remodeling, in which cephalad fluid shifts and altered hydrostatic gradients may contribute to changes in cerebrospinal fluid spaces, ventricular volume, and brain tissue configuration. Resting-state and cognitive electrophysiological changes likely represent additional adjustments in network regulation and resource allocation, but their functional interpretation remains more dependent on sleep, workload, recording conditions, and individual differences. Future studies should move beyond isolated pre-post comparisons and group averages toward standardized longitudinal designs spanning preflight, multiple in-flight time points, and postflight recovery. Synchronized assessment of brain function, behavior, sleep, and key environmental parameters should be used to characterize individual adaptation trajectories and determine which neural changes reflect successful compensation, which predict delayed recovery, and which have genuine value for brain-health risk prediction and individualized countermeasures.

Objective To investigate the perioperative outcomes and postoperative pregnancy outcomes of patients with intrauterine adhesions(IUA)treated by see-and-treat hysteroscopy and traditional hysteroscopy in transcervical resection of adhesions(TCRA).Methods A retrospective cohort study was performed on 485 patients with moderate-to-severe IUA who met the inclusion criteria and admitted in our hospital from January 2019 to December 2021.According to surgical approaches,the patients were assigned into a see-and-treat mode group and a traditional mode group.After clinical diagnosis(ultrasound and symptoms)of IUA,the patients from the former group received direct hysteroscopic adhesion separation surgery,and those of the latter group were diagnosed by outpatient hysteroscopy first and then hysteroscopic adhesion separation surgery.The perioperative indicators,postoperative three-dimensional transvaginal ultrasound(3D-TVUS)examination related characteristics,postoperative menstrual recovery,postoperative pregnancy outcomes,obstetric related complications,and neonatal outcomes were collected and compared between the 2 groups.Results Among the enrolled 485 patients,there were 277 in the see-and-treat group and 208 in the traditional group.The success rate of surgical treatment was 89.89%in the see-and-treat group and 92.79%in the traditional group,but no statistical difference was seen between them(Chi-square=1.234,P=0.267).3D-TVUS examination displayed that the see-and-treat mode group obtained better improvement of endometrial morphology,uterine morphology and menstruation after operation than the traditional group(P<0.001).The postoperative pregnancy rate was slightly higher in the see-and-treat group than the traditional mode group(58.84%vs 58.17%,P=0.882).However,the see-and-treat mode showed obvious advantage in the postoperative natural pregnancy rate,with a rate of 90.80%,obviously higher than that in the traditional mode group(81.82%,P=0.026).The live birth rate was 70.55%in the see-and-treat mode group,excluding 1 case with ongoing pregnancy in the second trimester and 1 case with ongoing pregnancy in the third trimester,and the rate was 74.38%in the traditional group,excluding 3 cases with ongoing pregnancy in the third trimester,but there was no statistical difference between the 2 groups(P=0.303).In terms of obstetric-related complications,there were 0 cases of blood transfusion during delivery hospitalization in the see-and-treat group,while there were 7 cases in the traditional group(P=0.003).In neonatal outcomes,the rate of transfer to the pediatric department was 10.43%in the see-and-treat mode group and 22.22%in the traditional mode group(P=0.021).For health economics,the see-and-treat hysteroscopy group demonstrated a significant advantage over the traditional hysteroscopy group(P<0.001).There was no significant difference in pain scores between the 2 groups.Conclusion The see-and-treat approach is a safe,feasible,and highly efficient strategy for integrating the diagnosis and treatment of IUA,enabling maximal minimization of surgical trauma while optimizing time and cost efficiency.

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.

Objective To analyze the long-term trend in the disease burden of congenital birth defects(CBDs)among neonates in China from 1990 to 2021,compare the trend with global patterns,and identify key subtypes along with their association with socioeconomic status to provide evidence for public health interventions.Methods Utilizing data from the Global Burden of Disease Study 2021(GBD 2021),we extracted indicators including disability-adjusted life years(DALYs),mortality,and prevalence for the neonatal period(＜28 days)in China,encompassing ten major CBD subtypes.Joinpoint regression analysis was employed to calculate annual percent changes and estimate annual percent changes(EAPC),with comparisons of subtype composition between 1990 and 2021.Nonlinear regression was used to assess the relationship between DALYs rates and the Socio-demographic Index(SDI).Results From 1990 to 2021,DALYs rates for neonatal CBDs declined significantly both globally and in China,with China's EAPC at-4.67％[95％CI:(—5.06,—4.28)],substantially exceeding the global average of-1.70％[95％CI:(—1.75,—1.64)].Congenital heart anomalies remained the primary burden,while neural tube defects and orofacial clefts in China showed notable reductions(EAPCs of-7.25％and-11.22％,respectively).However,DALYs rates for congenital musculoskeletal and limb anomalies exceeded global expected levels.A resurgence in the prevalence was observed post-2015,with higher burdens in males.DALYs rates exhibited a negative correlation with SDI.Conclusion China has achieved significant reductions in the neonatal CBDs burden,surpassing global trends;yet challenges persist in managing congenital heart anomalies and musculoskeletal defects.Future efforts should focus on enhancing early screening,surgical interventions,and regional equity to align with global health objectives.