Objective To construct a lung cancer surgery-oriented disease-specific database covering the entire perioperative care pathway, thereby improving the quality and usability of key surgical data elements. Methods Real-world clinical data were extracted from a single-center thoracic surgery department. A standardized data model was established based on the open electronic health record (openEHR) standard. Large language model (LLM), optical character recognition (OCR), and artificial intelligence (AI)-driven techniques were employed to extract, structure, and perform quality control on unstructured clinical narratives, imaging reports, and radiological data, with a focus on capturing surgically relevant perioperative indicator. Results A multimodal database comprising 19 917 patients was established, including 7 930 males and 11 987 females, with ages ranging from 15 to 97 (61.7±9.7) years. The database includes 582 structured data variables, textual report data corresponding to 69 clinical indicators, 13 000 pulmonary function test PDF reports, and chest CT imaging data from 16 884 patients. This database comprehensively covers major information relevant to surgical diagnosis and treatment of lung cancer, significantly improving the completeness and granularity of surgical detail data. Large language models (LLMs) and optical character recognition (OCR) technologies enhanced the efficiency of converting unstructured data into structured formats, while a multi-level manual verification process ensured data accuracy and traceability. The database supports real-world research including comparisons of surgical procedures, prediction of postoperative complications, prognosis assessment, and multimodal data association analyses.

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.

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.

Periodontitis is a common oral disease caused by bacteria coupled with an excessive host immune response. Stem cell therapy can be a promising treatment strategy for periodontitis, but the relevant mechanism is complicated. This study aimed to explore the therapeutic potential of mitochondria from human embryonic stem cell-derived mesenchymal stem cells (hESC-MSCs) for the treatment of periodontitis. The gingival tissues of periodontitis patients are characterized by abnormal mitochondrial structure. Human gingival fibroblasts (HGFs) were exposed to 5 μg/mL lipopolysaccharide (LPS) for 24 h to establish a cell injury model. When treated with hESC-MSCs or mitochondria derived from hESC-MSCs, HGFs showed reduced expression of inflammatory genes, increased adenosine triphosphate (ATP) level, decreased reactive oxygen species (ROS) production, and enhanced mitochondrial function compared to the control. The average efficiency of isolated mitochondrial transfer by hESC-MSCs was determined to be 8.93%. Besides, a therapy of local mitochondrial injection in mice with LPS-induced periodontitis showed a reduction in inflammatory gene expression, as well as an increase in both the mitochondrial number and the aspect ratio in gingival tissues. In conclusion, our results indicate that mitochondria derived from hESC-MSCs can reduce the inflammatory response and improve mitochondrial function in HGFs, suggesting that the transfer of mitochondria between hESC-MSCs and HGFs serves as a potential mechanism underlying the therapeutic effect of stem cells.
Humans ; Gingiva/cytology* ; Fibroblasts/metabolism* ; Mitochondria/physiology* ; Mesenchymal Stem Cells/cytology* ; Animals ; Periodontitis/therapy* ; Mice ; Reactive Oxygen Species/metabolism* ; Inflammation ; Lipopolysaccharides ; Human Embryonic Stem Cells/cytology* ; Cells, Cultured ; Adenosine Triphosphate/metabolism* ; Male

BACKGROUND: Spicy food consumption has been reported to be inversely associated with mortality from multiple diseases. However, the effect of spicy food intake on the incidence of vascular diseases in the Chinese population remains unclear. This study was conducted to explore this association. METHODS: This study was performed using the large-scale China Kadoorie Biobank (CKB) prospective cohort of 486,335 participants. The primary outcomes were vascular disease, ischemic heart disease (IHD), major coronary events (MCEs), cerebrovascular disease, stroke, and non-stroke cerebrovascular disease. A Cox proportional hazards regression model was used to assess the association between spicy food consumption and incident vascular diseases. Subgroup analysis was also performed to evaluate the heterogeneity of the association between spicy food consumption and the risk of vascular disease stratified by several basic characteristics. In addition, the joint effects of spicy food consumption and the healthy lifestyle score on the risk of vascular disease were also evaluated, and sensitivity analyses were performed to assess the reliability of the association results. RESULTS: During a median follow-up time of 12.1 years, a total of 136,125 patients with vascular disease, 46,689 patients with IHD, 10,097 patients with MCEs, 80,114 patients with cerebrovascular disease, 56,726 patients with stroke, and 40,098 patients with non-stroke cerebrovascular disease were identified. Participants who consumed spicy food 1-2 days/week (hazard ratio [HR] = 0.95, 95% confidence interval [95% CI] = [0.93, 0.97], P <0.001), 3-5 days/week (HR = 0.96, 95% CI = [0.94, 0.99], P = 0.003), and 6-7 days/week (HR = 0.97, 95% CI = [0.95, 0.99], P = 0.002) had a significantly lower risk of vascular disease than those who consumed spicy food less than once a week ( Ptrend <0.001), especially in those who were younger and living in rural areas. Notably, the disease-based subgroup analysis indicated that the inverse associations remained in IHD ( Ptrend = 0.011) and MCEs ( Ptrend = 0.002) risk. Intriguingly, there was an interaction effect between spicy food consumption and the healthy lifestyle score on the risk of IHD ( Pinteraction = 0.037). CONCLUSIONS Our findings support an inverse association between spicy food consumption and vascular disease in the Chinese population, which may provide additional dietary guidance for the prevention of vascular diseases.
Humans ; Male ; Female ; Prospective Studies ; Middle Aged ; Aged ; Vascular Diseases/etiology* ; Risk Factors ; China/epidemiology* ; Adult ; Proportional Hazards Models ; Cerebrovascular Disorders/epidemiology* ; East Asian People

Objective To discuss the application value of CT and DSA multimodal image fusion technology in endovascular interventional therapy for arterial occlusive lesions of lower extremities and to evaluate its efficacy and safety so as to provide a scientific basis for clinical decision-making.Methods A total of 283 lower limbs with arterial complete occlusive lesions,who received treatment at Affiliated Baiyun Hospital of Guizhou Medical University hospital from January 2020 to December 2023,were selected for this study.The 283 diseased lower limbs were randomly divided into study group(n=142)and control group(n=141).In the study group the endovascular interventional therapy assisted by CT and DSA multimodal image fusion technology was adopted,while in the control group the traditional DSA-guided endovascular interventional therapy was employed.The imaging parameters,surgical success rates,X-ray exposure doses,time spent for operation,incidence of postoperative complications,changes of ankle-brachial index(ABI),primary patency rate,assisted primary patency rate,and secondary patency rate were compared between the two groups.Results The surgical success rate in the study group was 96.47％,which was significantly higher than 87.94％in the control group(P＜0.05).The mean time spent for operation in the study group was(125.42±23.74)minutes,which was shorter than(147.81±29.33)minutes in the control group.The mean X-ray exposure dose in the study group was(2 856.34±427.82)mGy·cm2,which was lower than(3 674.53±512.60)mGy·cm2 in the control group.The incidence of postoperative complications in the study group was 4.23％,which was significantly lower than 12.57％in the control group(P＜0.05).The ABI values of the affected limbs in the study group and control group increased from preoperative(0.65±0.15)and(0.60±0.18)respectively to postoperative(1.09±0.32)and(0.90±0.28)respectively.The postoperative ABI value in the study group was higher than that in the control group(P＜0.05).The postoperative 12-month primary patency rate,assisted primary patency rate and secondary patency rate in the study group were 78.17％,85.92％and 90.14％respectively,which were better than 67.38％,75.89％and 80.85％respectively in the control group.Conclusion For arterial occlusive lesions of lower extremities,endovascular interventional therapy with the help of CT and DSA multimodal image fusion technology has high surgical success rates,low incidence of complications,and satisfactory revascularization rate.This technology provides new idea and method for the treatment of arterial occlusive lesions of lower extremities with high clinical safety.Therefore,this technology is worthy of clinical promotion and application.

The success of"New Medical Sciences"in higher education requires effective tools in evalua-ting students'performance in courses.Previously,we reported a teamwork(T),critique(C)and ap-preciation(A)(TCA)ideological and political model,a teaching model widely applied in Basic Medical courses.TCA is an abbreviation derived from Tricarboxylic Acid Cycle in Biochemistry as an analogy for nurturing the abilities of thinking and teamwork(T),critique(C)and appreciation(A),which hope-fully could provide students with moral norms for cognition,science and life.This paper further explores the tools to assess the educational outcomes of the TCA model,by which teachers can collect feedback and reflect on teaching quality and effectiveness.Addressing the challenges of individual differences in large classes,fragmented learning feedback,and the difficulty of measuring meta-cognition in educational evaluation,this study employs strategies of value-added assessment,matrix assessment and norm-transfer-able assessment to evaluate the TCA abilities from the aspects of thinking quality,thinking creativity,co-operation ability,iterative thinking,dialectical thinking and job responsibility.By modifying/using 18 e-valuation tools in Education and Psychology,we have established a rubric system composed of 30 primary indicators(with 11 newly designed,10 partly modified and 9 directly adopted),along with 49 secondary indicators and 98 tertiary indicators to enhance the feasibility of the evaluation process.This rubric sys-tem was applied to Biochemistry teaching among the five-year-program undergraduates at Air Force Medi-cal University.Specifically,thinking and teamwork are evaluated by creative works from"the magic bio-chemical-circle",while critiques are assessed in large classes under the guidance of basic and clinical teachers,coupled with appreciation measured by job responsibility in a task-driven virtual reality(VR)project.The results indicate that Biochemistry teaching not only accumulates knowledge in students,but also achieves the goals in nurturing values and cognition.The inclusion of creative performance evalua-tion,cooperative learning and clinical case studies,can enhance students'interpersonal skills,coopera-tion,quality of thinking,creative thinking,iterative thinking and dialectical thinking to varying degrees.TCA-based Biochemistry teaching has a long-lasting impact on character education,and is capable of in-ducing positive long-term changes in students'cognition and lifestyle.Taken together,with the help of this rubric system,teachers can promptly acknowledge the effectiveness of their teaching,thereby facilita-ting their teaching strategies.

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.