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.

Allogeneic haematopoietic stem cell transplantation(allo-HSCT)is the most effective curative treatment for hematologic malignancies.Its efficacy hinges on eliminating primary hematological disorders and restoring bone marrow hematopoiesis during conditioning,as well as leveraging the graft-versus-leukemia(GVL)effect.However,acute graft-versus-host disease(aGVHD)remains a significant complication following allo-HSCT,substantially affecting patient survival and quality of life.Current preclinical studies focus on strategies to mitigate aGVHD while preserving adequate GVL effects to improve transplant outcomes.This review summarizes recent preclinical research findings in this field,emphasizing the regulatory roles and specific molecular mechanisms of T cells,antigen-presenting cells,myeloid-derived suppressor cells,and mesenchymal stem cells in aGVHD.It further highlights the latest therapeutic strategies for aGVHD from preclinical studies,aiming to provide valuable insights for researchers and clinicians to develop more effective therapeutic targets and 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.

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.

Allogeneic haematopoietic stem cell transplantation(allo-HSCT)is the most effective curative treatment for hematologic malignancies.Its efficacy hinges on eliminating primary hematological disorders and restoring bone marrow hematopoiesis during conditioning,as well as leveraging the graft-versus-leukemia(GVL)effect.However,acute graft-versus-host disease(aGVHD)remains a significant complication following allo-HSCT,substantially affecting patient survival and quality of life.Current preclinical studies focus on strategies to mitigate aGVHD while preserving adequate GVL effects to improve transplant outcomes.This review summarizes recent preclinical research findings in this field,emphasizing the regulatory roles and specific molecular mechanisms of T cells,antigen-presenting cells,myeloid-derived suppressor cells,and mesenchymal stem cells in aGVHD.It further highlights the latest therapeutic strategies for aGVHD from preclinical studies,aiming to provide valuable insights for researchers and clinicians to develop more effective therapeutic targets and strategies.

Circular RNAs(CircRNAs)are a class of non-coding RNAs with a covalently closed-loop structure,high stability,and tissue specificity,with the production mechanisms different from linear RNAs.Recent studies have discovered that some CircRNAs can encode proteins via cap-independent translation mechanisms such as internal ribosome entry site,N6-methyladenosine,and rolling loop translation.The encoded proteins regulate homologous linear proteins or downstream signaling pathways via protein bait or other mecha-nisms,thereby exerting biological functions.Studies have shown that CircRNAs play a role in various diseases,especially in tumor progression,proliferation,invasion,and metastasis and immune regulation.Therefore,by elucidating the expression and roles of proteins encoded by CircRNAs in tumorigenesis and development,this pa-per is expected to provide new tumor markers and potential targets for tumor diagnosis and treatment.

Objective:To investigate the clinical effect of the anterolateral femoral perforator flap（ALTP）pedicled with the oblique branch of lateral circumflex femoral artery（LCFA）assisted by CT angiography（CTA）examination for repairing soft tissue defects of limbs.Methods:A retrospective case series study was made on 51 patients with soft tissue defects of limbs treated in Ningbo No.6 Hospital from March 2015 to March 2020，including 31 males and 20 females at age of 26-63 years［（42.0±8.9）years］. The defects were located at the forearm in 15 patients，at the hand in 13，at the lower leg in 15 and at the ankle in 8. The size of defects ranged from 9 cm×6 cm to 18 cm×10 cm，with the size of flaps from 10 cm×6 cm to 20 cm×12 cm. A total of 33 patients were examined with CTA scanning and Doppler ultrasound（CTA group）and 18 patients with Doppler ultrasound（Doppler group）. All patients underwent debridement and negative pressure closed drainage（VSD）at stage I and were repaired by ALTP pedicled with the oblique branch of LCFA at stage II. The diameter and length of the vessel pedicle was recorded in CTA group before operationand in both groups during operation. The time of flap harvesting in both groups was recorded during operation. The survival of the flap in both groups was observed one week after operation. Zhang Hao's scoring standard was applied to evaluate the outcome at the last follow-up.Results:All patients were followed up for 6-12 months［（9.1±1.5）months］. In CTA group，the diameter of LCFA vessel pedicle measured before operation had no significant difference from that during operation（ P>0.05），while the length of LCFA vessel pedicle before operation［（12.3±2.1）cm］was longer than（10.9±2.2）cm during operation（ P<0.05）. The two group showed no significant differences in the diameter and length of LCFA vessel pedicle during operation（ P>0.05）. The time of flap harvesting in CTA group was（38.5±6.2）minutes，significantly shorter than（51.4±8.4）minutes in Doppler group（ P<0.05）. One week after operation，all flaps survived. Two patients developed flap arterial congestion in CTA group，among whom one survived after surgical revision and one with partially necrosis was healed after dressing change. One patient was found with flap arterial congestion with partial necrosis in Doppler group，who was healed after dressing change. There was no significant difference in postoperative flap arterial congestion between the two groups（ P>0.05）. The patients' satisfaction score in CTA group was（8.5±1.5）points at the last follow-up，higher than（7.4±2.0）points in Doppler group（ P<0.05）. Conclusion:For repairing soft tissue defects of limbs，free ALTP pedicled with the oblique branch of LCFA assisted by three- dimensional CT angiography can accurately get the information of perforator，shorten the flap harvesting time，and obtain satisfactory clinical results as compared to Doppler ultrasound.