Objective·To explore the value of combined diaphragm and intercostal muscle ultrasound assessment compared with conventional diaphragm ultrasound in predicting the weaning outcome in mechanically ventilated patients with sepsis.Methods·Mechanically ventilated patients with sepsis,consecutively admitted to the Department of Critical Care Medicine of Affiliated Hospital of Nantong University from October 2022 to December 2023,were selected.During the peri-weaning period,after the patient's sepsis condition improved and the patient passed the spontaneous breathing trial(SBT),ultrasound evaluation of respiratory muscles was performed by ultrasound qualified personnel with ultrasound qualification and experience in bedside ultrasound examination.Diaphragm excursion(DE),thickening fraction of diaphragm(TFD),and thickening fraction of intercostal muscle(TFic)were measured,respectively.The patients were divided into a successful weaning group(n=114)and a failed weaning group(n=24)according to the weaning results.Receiver operating characteristic(ROC)curves were used to analyze the value of diaphragm ultrasound and intercostal muscle ultrasound,alone and in combination,in predicting ventilator weaning outcome.Results·TFic and TFic/TFD were significantly higher in the failed weaning group during SBT than in the successful weaning group(all P<0.05).The areas under the ROC curve(AUROC)of DE,TFD,and TFic to predict weaning failure in mechanically ventilated patients with sepsis during the period of SBT were 0.689(0.591?0.776),0.657(0.557?0.747),and 0.769(0.676?0.846),respectively,whereas the combined indexes TFic/TFD and TFic&TFD_mix had AUROCs of 0.867(0.786?0.925)and 0.860(0.778?0.920),respectively.TFic/TFD with a cutoff value of>0.95 had a sensitivity of 86.7%and a specificity of 75.3%in predicting weaning failure,and TFic&TFD_mix with a cutoff value of>0.13 had a sensitivity of 86.6%and a specificity of 80.9%in predicting weaning failure.Moreover,the intercostal muscle ultrasonography method had an intra-observer intraclass correlation coefficient(ICC)of 0.890 and an extra-observer ICC of 0.876 for measurement reliability,which were both rated as good(P<0.001).Conclusion·Combined diaphragm and intercostal muscle ultrasonography provides a more comprehensive picture of the patient's overall respiratory muscles,and has a higher guiding value in predicting the weaning outcomes in mechanically ventilated patients with sepsis than diaphragm ultrasound alone.

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.

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.

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 value of combined diaphragm and intercostal muscle ultrasound assessment compared with conventional diaphragm ultrasound in predicting the weaning outcome in mechanically ventilated patients with sepsis.Methods·Mechanically ventilated patients with sepsis,consecutively admitted to the Department of Critical Care Medicine of Affiliated Hospital of Nantong University from October 2022 to December 2023,were selected.During the peri-weaning period,after the patient's sepsis condition improved and the patient passed the spontaneous breathing trial(SBT),ultrasound evaluation of respiratory muscles was performed by ultrasound qualified personnel with ultrasound qualification and experience in bedside ultrasound examination.Diaphragm excursion(DE),thickening fraction of diaphragm(TFD),and thickening fraction of intercostal muscle(TFic)were measured,respectively.The patients were divided into a successful weaning group(n=114)and a failed weaning group(n=24)according to the weaning results.Receiver operating characteristic(ROC)curves were used to analyze the value of diaphragm ultrasound and intercostal muscle ultrasound,alone and in combination,in predicting ventilator weaning outcome.Results·TFic and TFic/TFD were significantly higher in the failed weaning group during SBT than in the successful weaning group(all P<0.05).The areas under the ROC curve(AUROC)of DE,TFD,and TFic to predict weaning failure in mechanically ventilated patients with sepsis during the period of SBT were 0.689(0.591?0.776),0.657(0.557?0.747),and 0.769(0.676?0.846),respectively,whereas the combined indexes TFic/TFD and TFic&TFD_mix had AUROCs of 0.867(0.786?0.925)and 0.860(0.778?0.920),respectively.TFic/TFD with a cutoff value of>0.95 had a sensitivity of 86.7%and a specificity of 75.3%in predicting weaning failure,and TFic&TFD_mix with a cutoff value of>0.13 had a sensitivity of 86.6%and a specificity of 80.9%in predicting weaning failure.Moreover,the intercostal muscle ultrasonography method had an intra-observer intraclass correlation coefficient(ICC)of 0.890 and an extra-observer ICC of 0.876 for measurement reliability,which were both rated as good(P<0.001).Conclusion·Combined diaphragm and intercostal muscle ultrasonography provides a more comprehensive picture of the patient's overall respiratory muscles,and has a higher guiding value in predicting the weaning outcomes in mechanically ventilated patients with sepsis than diaphragm ultrasound alone.

Objective:To investigate the diagnostic value of MRI and CT for the liver space-occupying lesions.Methods:The clinical data of 70 cases of patients with liver space-occupying lesions in our hospital from June 2012 to May 2016 were divided into two groups and retrospectively analyzed.35 cases underwent contrast enhanced ct scans (CT group),and others underwent dynamic contrast-enhanced MR imaging(MRI group).The pathological diagnosis,number of lesions and lesions diameter were ompared between two groups.Results:No significant difference was found in the pathological diagnosis,number of lesions(71 vs 70) and lesions diameter(2.25 ± 2.01 cm vs 2.19± 1.98 cm) between two groups(P＞0.05).As the gold standard by pathological diagnosis results,correct diagnostic rate of MRIgroup were 85.71％,which was 77.14％ CT group and lower than that of the MRI group,but no significant difference was found between two groups (P＞0.05).The incidence of adverse reactions in CT group was significantly higher than that of the MRI group (P＞0.05).Conelusion:Both CT and MRI enhancement scanning have higher diagnostic value for the liver space-occupying lesions,but MRI enhancement scanning has higher safety and tolerability.

Objective Little is known about the effects of antioxidant on the micturition function in diabetic cystopathy .In this study, we investigated the effects of antioxidant sulforaphane on bladder micturition function in diabetes mellitus ( DM)mice with bladder underactivity . Met hods We established DM models in mice by intraperitoneal injection of a single dose of streptozotocin (STZ)at 65 mg/kg and randomly divided them into three groups , sulforaphane treatment (n=10), vehicle treatment (n=10), and DM model (n=10), with another 10 normal healthy mice included as blank controls.At 24 weeks of the experiment, we obtained and analyzed such indexes of mice as the body weight , fasting blood glucose (FBG), 24-hour urine volume (24 h UV) and bladder wet weight ( BWW ) , results of cystometrography , and cystometric parameters including intercontraction interval ( ICI ) , maximum bladder pressure during micturition ( Pmax ) , maximum cystometric capacity (MCC), void volume (VV), post-void residual urine vol-ume (PVR) and residual urine rate (RUR). Results Compared with the blank controls , the DM models with bladder underactivity showed significantly increases in BWW ([67.96 ±2.35]mg), 24 h HU ([22.47 ±1.93]mL), MCC ([0.70 ±0.03]mL), VV (0[.23 ±0.01]mL), PVR ([0.49 ±0.02]mL), RUR ([70.10 ± 0.80]%), and ICI, but a remarkable decrease in Pmax .Sulforaphane treatment significantly reduced BWW ([576.9 ±2.41]mg), 24 h HU ([16.27 ±1.51] mL), MCC ([0.54 ±0.03]mL), PVR ([0.34 ±0.02]mL), RUR ([62.71 ±1.26]%), and ICI of the diabetic mice . Conclusion Sulforaphane could improve bladder micturition function in mice with STZ-induced DM , which might be related to its action mechanism of antioxidative stress damage .