Premature ovarian insufficiency （POI） is a reproductive endocrine disorder that severely affects female reproductive health. However， there remains a lack of clinically effective and safe therapeutic options to restore ovarian function. Traditional Chinese medicine （TCM）， with its advantages of multi-component and multi-target holistic regulation， has shown unique potential in the prevention and treatment of POI. Among TCM monomers， curcumin， resveratrol， and icariin exert antioxidant， anti-inflammatory， and anti-ovarian granulosa cell apoptosis effects by regulating signaling pathways such as nuclear factor-erythroid 2-related factor 2/heme oxygenase-1， phosphoinositide 3-kinase/protein kinase B， and mitogen-activated protein kinase. Single TCM herbs， including American ginseng， Cuscutae Semen， and Cistanches Herba， can regulate sex hormone levels， and alleviate ovarian oxidative stress and immune damage. TCM compound formulas such as Yijing decoction， Siwu decoction， Zuogui pill， and Kuntai capsule modulate multiple signaling pathways， including vascular endothelial growth factor/vascular endothelial growth factor receptor-2， phosphoinositide 3-kinase/protein kinase B/mammalian target of rapamycin， and silence information regulator 1/p53， thereby promoting angiogenesis， inhibiting mitochondrial apoptosis， and regulating autophagy and ferroptosis， which ultimately delay follicular atresia and improve ovarian reserve function. Further studies are suggested to apply artificial intelligence to deeply explore the underlying mechanisms and carry out high-quality clinical trials， so as to promote the standardized application of TCM in the prevention and treatment of POI.

Objective To construct and validate a prediction model for urosepsis in patients after upper urinary tract stone surgery using various machine learning algorithms.Methods A total of 7 464 upper urinary tract stone patients who underwent surgery at the Sixth Affiliated Hospital of Guangxi Medical University from Jun.2018 to Jun.2023 were enrolled and randomly assigned to training(5 224 cases)or validation sets(2 240 cases)at a ratio of 7∶3.Among them,622(8.33%)cases developed urosepsis postoperatively.Six machine learning algorithms,including extreme gradient boosting(XGBoost),logistic regression,light gradient boosting machine(LightGBM),random forest(RF),adaptive boosting(AdaBoost),and gradient boosting decision tree(GBDT),were used to construct prediction models for postoperative urosepsis.The model's predictive ability and clinical benefits were evaluated using receiver operating characteristic(ROC)curves,Shapley additive explanation(SHAP)analysis,calibration curves,and decision curve analysis(DCA).Results The clinical features included body mass index(BMI),number of surgeries,heart rate,Barthel index,venous thrombo embolism(VTE)risk assessment,gender,American Society of Anesthesiologists(ASA)grade,urinary nitrite,and urinary leukocyte in the models.In the training set,the XGBoost,LightGBM,and RF models performed excellently,with area under curve(AUC)values of ROC curves reaching 1.00.In the validation set,the logistic regression model performed the best,with an AUC value of ROC curve of 0.76,showing good predictive stability and calibration.The AdaBoost and GBDT models followed with AUC values of 0.74 and 0.75,respectively,while the AUC values of the LightGBM,XGBoost,and RF models were 0.71,0.70,and 0.68.In terms of model interpretability,SHAP analysis showed the contribution of variables in a descending order as:heart rate,urinary leukocytes,gender,BMI,Barthel index,VTE risk assessment,urinary nitrite,number of surgeries,and ASA grade.Conclusion A logistic regression model for early risk prediction of postoperative urosepsis in upper urinary tract stone patients has been successfully constructed.This model has good predictive performance and calibration,and can effectively assist clinical diagnosis.