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 establish TLC identification method,HPLC fingerprint,chemical pattern recognition,and multi-components quantitative analysis methods of Spatholobus suberectus,and identify the quality of 16 batches of Spatholobus su-berectus from different origins.Methods The TLC method was used to identify Spatholobus suberectus qualitatively.HPLC fin-gerprint of Spatholobus suberectus was established.The quality of Spatholobus suberectus was evaluated by chemical pattern recogni-tion technologies,and the contents of Gallocatechin,protocatechuic acid,catechin,epigallocatechin and fermononetin were deter-mined by HPLC.Results TLC method of Spatholobus suberectus was established.Ten common peaks were identified in 16 bat-ches of Spatholobus suberectus,and 6 components were identified by reference substances.The similarity of fingerprint was 0.769-0.990,indicating good similarity.The samples were divided into 3 groups by cluster analysis.Results of the principal component a-nalysis showed that the top 3 samples in the list of comprehensive scores were S16、S1 1、S14.Marker compounds that cause the quality difference of Spatholobus suberectus were screened out through the orthogonal partial least squares discriminant analysis,which were fermononetin,peaks 7 and epigallocatechin.Gallocatechin,protocatechuic acid,catechin,epigallocatechin,and ferm-ononetin had a good linearity in the concentration range(r＞0.999).The content determination analysis showed that there were sig-nificant differences in the contents of the 5 index components among Spatholobus suberectus from different origins.Conclusion The established TLC and HPLC fingerprints of Spatholobus suberectus were stable and reliable,and the HPLC method for multiple active components method provides scientific support for improving the quality control method of Spatholobus suberectus.

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 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 establish TLC identification method,HPLC fingerprint,chemical pattern recognition,and multi-components quantitative analysis methods of Spatholobus suberectus,and identify the quality of 16 batches of Spatholobus su-berectus from different origins.Methods The TLC method was used to identify Spatholobus suberectus qualitatively.HPLC fin-gerprint of Spatholobus suberectus was established.The quality of Spatholobus suberectus was evaluated by chemical pattern recogni-tion technologies,and the contents of Gallocatechin,protocatechuic acid,catechin,epigallocatechin and fermononetin were deter-mined by HPLC.Results TLC method of Spatholobus suberectus was established.Ten common peaks were identified in 16 bat-ches of Spatholobus suberectus,and 6 components were identified by reference substances.The similarity of fingerprint was 0.769-0.990,indicating good similarity.The samples were divided into 3 groups by cluster analysis.Results of the principal component a-nalysis showed that the top 3 samples in the list of comprehensive scores were S16、S1 1、S14.Marker compounds that cause the quality difference of Spatholobus suberectus were screened out through the orthogonal partial least squares discriminant analysis,which were fermononetin,peaks 7 and epigallocatechin.Gallocatechin,protocatechuic acid,catechin,epigallocatechin,and ferm-ononetin had a good linearity in the concentration range(r＞0.999).The content determination analysis showed that there were sig-nificant differences in the contents of the 5 index components among Spatholobus suberectus from different origins.Conclusion The established TLC and HPLC fingerprints of Spatholobus suberectus were stable and reliable,and the HPLC method for multiple active components method provides scientific support for improving the quality control method of Spatholobus suberectus.

In this study, sonography video urodynamic studies (SVUDS), which combined synchronically urodynamic studies with trans-perineal and trans-abdominal sonography, were used to detect female bladder outlet obstruction (FBOO). The dynamic changes of urethra and surrounding pelvic floor structure during storage and voiding phase were observed by SVUDS and the causes of FBOO were analyzed. And the findings were as follows: 13 patients showed organ prolapse, there was an urethral angulation deformity during urination; 5 cases had abnormal urination as the urethral opening was not good in the middle of urination period; 4 cases had urethral stricture, as the proximal end of the obstruction dilated during urination, and the obstruction site showed no relaxation; 1 case had primary bladder neck obstruction with an incomplete opening of the bladder neck during urination; 3 cases had idiopathic bladder outlet obstruction and the sphincter of bladder neck and urethra opened well during urination.

Objective:To explore the distribution of different subsets of monocyte in peripheral blood of patients with thyroid-associated ophthalmopathy (TAO).Methods:A cross-sectional study was performed.Fifty-nine TAO patients and 30 healthy subjects were recruited continuously in Zhongshan Ophthalmic Center from January 2017 to December 2019.Clinical data of subjects were recorded, and the severity and activity of TAO were graded based on the criteria of NOSPECS and CAS.TAO patients were grouped according to clinical activity of TAO, and the patients were treated by triamcinolone acetonide (TA) injection or methylprednisolone pulse therapy (MPT) accordingly.Peripheral blood of the subjects was collected and monocytes were isolated.The proportion of different monocyte subsets was assayed by a flow cytometry.The differences in distribution of monocyte subsets between TAO group and normal control group, stable TAO group and active TAO group, TA injected group and MPT treated group were compared and analyzed.The study protocol was approved by the Ethics Committee of Zhongshan Ophthalmic Center, Sun Yat-Sen University (No.2014MEKY005), and the written informed consent was obtained from each subject before any medical intervention.Results:The proportion of classical monocyte (CMo) subset in TAO group was (81.77%±5.53)%, which was significantly lower than (84.35±5.83)% in the normal control group ( P=0.034); the proportion of intermediate monocyte (IMo) subset in the TAO group was (10.17±4.19)%, which was significantly higher than (7.69±4.09)% in the normal control group ( P=0.006); no significant difference was found in the proportion of non-classical monocyte (NMo) subset between the two groups ( P=0.892). The proportion of CMo subset in the active TAO group was (77.29±5.80)%, which was significantly lower than (82.64±5.03)% in the stable TAO group ( P<0.01), and the proportion of IMo subset in the active TAO group was (13.79±4.82)%, which was significantly higher than (9.20±3.56)% in the stable TAO group ( P<0.01); no significant change was found in the proportion of NMo subset between the two groups ( P=0.283). There was no difference in the proportion of different TAO subsets before and after TA injection ( P>0.05). In MPT treated group, the proportion of CMo subset in TAO patients was significantly increased and the proportion of IMo subset was significantly decreased (both at P<0.05); there was no significant difference in proportion of NMo subset before and after MPT treatment ( P=0.187). Conclusions:IMo subset is enriched in patients with TAO, and the IMo subset content varies over the disease activity.MPT may inhibit the shift of CMo subset towards IMo subset.