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.

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 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.

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 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 explore the distribution and clearance of 99mTc labeled diethylenetriamine pentaacetic acid(99mTc-DTPA)in different brain regions of adult rats after administration through brain extracellular space(ECS)pathway.Methods:After the injection of a volume of 2 μL and radioactive activity of about 3.7 MBq(100 μCi)of 99mTc-DTPA into the caudate nucleus and thalamus of SD rats through stereotactic positioning of rat brain,the single photon emission computed tomography/computed tomography(SPECT/CT)for small animals was used for imaging at different time points,and the dyna-mic distribution and clearance of the tracer in the whole body were observed continuously.The SD rats were injected with 99mTc-DTPA into thalamus and caudate nucleus respectively for biological distribution in vivo.They were put to death 4 h later.Their blood and urine were collected.The brain,cerebellum,heart,liver,spleen,lung,and kidney were taken and weighed by γ counter to measure its radioactivity.Results:SPECT/CT imaging results showed that after 99mTc-DTPA was administered through brain ECS,the radioactivity was concentrated in the brain,kidney and bladder.The tracer administered to the left caudate nucleus was preferentially drained to the right cerebellum,while the tracer administered to the right caudate nucleus was preferentially drained to the left cerebellum.There was a phenomenon of"con-tralateral cerebellar dominant drainage"in the caudate nucleus.The thalamic area preferentially drained to the ipsilateral cerebellum after administration.Four hours after administration via ECS,high radioac-tive uptake appeared in urine,cerebellum and brain,followed by blood and kidney.The radioactive up-take values of heart,liver,spleen and lung were low,which were mainly excreted through urinary sys-tem.Conclusion:Intracerebral ECS administration is a promising method of administration,but there are significant differences in distribution and clearance in different brain regions.This study further ex-pands the content and significance of"ECS regions",and also provides an important theoretical founda-tion for the treatment of encephalopathy and the research of new drugs through brain ECS in the future.

[Objective]To establish a risk recognition model for coronary artery stenosis by using a machine learning method and to identify the key causative factors.[Methods]Patients aged≥18 years,diagnosed with coronary heart disease through coronary angiography from January 2013 to May 2020 in two prominent hospitals in Shanxi Province,were continuously enrolled.Logistic regression,back propagation neural network(BPNN),and random forest(RF)algorithms were used to construct models for detecting the causative factors of coronary artery stenosis.Sensitivity(TPR),specificity(TNR),accuracy(ACC),positive predictive value(PV+),negative predictive value(PV-),area under subject operating characteristic curve(AUC),and calibration curve were used to compare the discrimination and calibration performance of the models.The best model was then employed to predict the main risk variables associated with coronary stenosis.[Results]The RF model exhibited superior comprehensive performance compared to logistic regression and BPNN models.The TPR values for logistic regression,BPNN,and RF models were 75.76%,74.30%,and 93.70%,while ACC values were 74.05%,72.30%,and 79.49%,respectively.The AUC values were:logistic regression 0.739 9;BPNN 0.723 1;RF 0.752 2.Manifestations such as chest pains,abnormal ST segments on ECG,ventricular premature beats with hypertension,atrial fibrillation,regional wall motion abnormalities(RWMA)by color echocardiography,aortic regurgitation(AR),pulmonary insufficiency(PI),family history of cardiovascular diseases,and body mass index(BMI)were identified as top ten important variables affecting coronary stenosis according to the RF model.[Conclusions]Random forest model shows the best comprehensive performance in identification and accurate assessment of coronary artery stenosis.The prediction of risk factors affecting coronary artery stenosis can provide a scientific basis for clinical intervention and help to formulate further diagnosis and treatment strategies so as to delay the disease progression.

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 evaluate the efficacy of proximal femoral universal nail(PFUN),a new type of intramedullary fixation system,in the treatment of acute unilateral intertrochanteric femoral fracture in elderly patients.Methods From January 2022 to January 2024,200 patients with acute unilateral femoral intertrochanteric fracture were treated in our department.After closed traction reduction,PFUN was implanted with small incisions.Unified rehabilitation plan was adopted after surgery.The functional evaluation was performed by using the Harris hip score system at the last follow-up.Results The operation time was 25-182 min(median,63.0 min).The intraoperative blood loss was 10-750 ml(median,50.0 ml).Intraoperative blood transfusion(suspension of red blood cells)was required in 40 cases(20％).The postoperative hospital stay was 1-15 d(mean,4.0±1.9 d).The postoperative femoral neck-shaft angle was 116.7°-140.1°(mean,132.4°±5.5°).The quality of fracture reduction on the first day after surgery showed 140 cases as excellent,54 cases acceptable,and 6 cases poor.Complications occurred in 13 cases,including superficial wound infection in 2 cases,who were cured by regular wound dressing change and antibiotic treatment,lower extremity deep vein thrombosis in 8 cases,who were given low molecular weight heparin anticoagulation treatment until improvement,pneumonia in 2 cases and urinary tract infection in 1 case,who were cured or improved after specialist treatment.At one month after surgery,the hip joint X-ray showed blurred fracture lines,callus formation at the fracture site,and no internal fixation failure.There was no internal fixation failure within 3 months after surgery.At six months after surgery,all fractures achieved healing without any failure of internal fixation.All the 200 cases were followed up for 12-29 months(mean,16.1±2.7 months).One case experienced internal fixation failure,and underwent head and neck screw resection,internal fixation removal and hip replacement.At the last follow-up,the postoperative recovery was satisfactory.The Harris score of hip joint was 70-94 points(mean,88.8±2.8 points),including 103 cases as excellent,92 cases good,and 5 cases fair,with an excellent and good rate of 97.5％(195/200).Conclusion PFUN is effective in the treatment of intertrochanteric femoral fractures in elderly patients,with reliable fixation,rapid postoperative recovery,and low failure rate of internal fixation,especially suitable for unstable cases with internal or external wall fractures.