Recent studies have demonstrated that dapagliflozin, a sodium-glucose cotransporter type 2 (SGLT2) inhibitor, prevents endothelial dysfunction; however, direct effects of dapagliflozin on the endothelium under oxidative stress and the underlying mechanism of action are not completely understood. This study aimed to define the role and related mechanisms of dapagliflozin in hydrogen peroxide (H₂O₂)-induced endothelial dysfunction. The endothelium-dependent vasorelaxation effect of dapagliflozin was assessed in an organ bath study. Endothelial dysfunction was assessed using protein expression level and phosphorylation of endothelial nitric oxide synthase (eNOS), nitric oxide (NO), reactive oxygen species (ROS), senescence-associated beta-galactosidase (SA-β-gal) activity, and senescence marker proteins (p21, p53). Co-immunoprecipitation and protein acetylation were performed to detect protein interactions. Dapagliflozin exerted a direct vasorelaxant effect in the aortic rings of C57BL/6 J mice. Furthermore, there was a significant improvement in endothelium-dependent vasorelaxation in dapagliflozin-treated diabetic mice compared to vehicle controls. Moreover, intracellular ROS levels and ONOO- levels, increased by H₂O₂, were reduced by dapagliflozin. Importantly, dapagliflozin inhibited H₂O₂-induced senescence in the human umbilical vein endothelial cells (HUVECs), as indicated by reduced SA-β-gal, p21, and p53. Mechanistically, dapagliflozin reversed the H₂O₂-mediated inhibition of eNOS serine phosphorylation and sirtuin 1 (SIRT1) expression in endothelial cells. In particular, SIRT1-mediated eNOS deacetylation is reportedly involved in dapagliflozin-enhanced eNOS activity. These findings indicate that dapagliflozin ameliorates endothelial dysfunction by restoring eNOS activity, restoring NO bioavailability, and reducing ROS generation via SIRT1 activation in oxidative stress-stimulated endothelial cells.

Aim: This study aimed to use machine learning algorithms to identify critical preoperative variables and predict the red blood cell (RBC) transfusion during or after liver transplantation surgery.Study Design and Methods: A total of 1,193 patients undergoing liver transplantation in three large tertiary hospitals in China were examined. Twenty-four preoperative variables were collected, including essential population characteristics, diagnosis, symptoms, and laboratory parameters. The cohort was randomly split into a train set (70%) and a validation set (30%). The Recursive Feature Elimination and eXtreme Gradient Boosting algorithms (XGBOOST) were used to select variables and build machine learning prediction models, respectively. Besides, seven other machine learning models and logistic regression were developed. The area under the receiver operating characteristic (AUROC) was used to compare the prediction performance of different models. The SHapley Additive exPlanations package was applied to interpret the XGBOOST model. Data from 31 patients at one of the hospitals were prospectively collected for model validation.Results: In this study, 72.1% of patients in the training set and 73.2% in the validation set underwent RBC transfusion during or after the surgery. Nine vital preoperative variables were finally selected, including the presence of portal hypertension, age, hemoglobin, diagnosis, direct bilirubin, activated partial thromboplastin time, globulin, aspartate aminotransferase, and alanine aminotransferase. The XGBOOST model presented significantly better predictive performance (AUROC: 0.813) than other models and also performed well in the prospective dataset (accuracy: 76.9%).Discussion: A model for predicting RBC transfusion during or after liver transplantation was successfully developed using a machine learning algorithm based on nine preoperative variables, which could guide high-risk patients to take appropriate preventive measures.

Background: The association between metabolic syndrome and the development of heart failure (HF) with preserved ejection fraction (HFpEF) has not been completely clarified.Aim: To evaluate the association between metabolic syndrome and the risk of HF hospitalization for patients with HFpEF.Methods: Patient data were obtained from the American cohort of the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial database. Data for the primary outcome (hospitalization for HF) and secondary outcomes (all-cause mortality, cardiovascular mortality, and all-cause hospitalization) were collected, and hazard ratios (HRs) for the patients with and without metabolic syndrome were analyzed by applying a multivariable Cox proportional hazard model.Results: Among the 1,548 total participants, 1,197 had metabolic syndrome. The patients with metabolic syndrome exhibited worse heart function and a lower quality of life than those without metabolic syndrome. During the 3.3 years of follow-up, 351 patients were hospitalized for HF. After a multivariable adjustment, the risk of hospitalization for HF and all-cause hospitalization (adjusted HR = 1.42, 95% CI: 1.01–2.00; p = 0.042 and adjusted HR = 1.27; 95% CI: 1.04–1.54; p = 0.017, respectively) were independently associated with HFpEF for the patients with metabolic syndrome. In addition, the risks of HF hospitalization and all-cause hospitalization among 267 propensity score-matched patients were higher for patients with metabolic syndrome (HR = 1.53, 95% CI = 1.05–2.23, and p = 0.025 and HR = 1.34, 95% CI = 1.08–1.67, and p = 0.009, respectively).Conclusion: The risks of HF hospitalization and all-cause hospitalization were higher for patients with HFpEF having metabolic syndrome than for those without metabolic syndrome.

Background: Selecting features related to postoperative infection following cardiac surgery was highly valuable for effective intervention. We used machine learning methods to identify critical perioperative infection-related variables after mitral valve surgery and construct a prediction model.Methods: Participants comprised 1223 patients who underwent cardiac valvular surgery at eight large centers in China. The ninety-one demographic and perioperative parameters were collected. Random forest (RF) and least absolute shrinkage and selection operator (LASSO) techniques were used to identify postoperative infection-related variables; the Venn diagram determined overlapping variables. The following ML methods: random forest (RF), extreme gradient boosting (XGBoost), Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), AdaBoost, Naive Bayesian (NB), Logistic Regression (LogicR), Neural Networks (nnet) and artificial neural network (ANN) were developed to construct the models. We constructed receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) was calculated to evaluate model performance.Results: We identified 47 and 35 variables with RF and LASSO, respectively. Twenty-one overlapping variables were finally selected for model construction: age, weight, hospital stay, total red blood cell (RBC) and total fresh frozen plasma (FFP) transfusions, New York Heart Association (NYHA) class, preoperative creatinine, left ventricular ejection fraction (LVEF), RBC count, platelet (PLT) count, prothrombin time, intraoperative autologous blood, total output, total input, aortic cross-clamp (ACC) time, postoperative white blood cell (WBC) count, aspartate aminotransferase (AST), alanine aminotransferase (ALT), PLT count, hemoglobin (Hb), and LVEF. The prediction models for infection after mitral valve surgery were established based on these variables, and they all showed excellent discrimination performance in the test set (AUC > 0.79).Conclusions: Key features selected by machine learning methods can accurately predict infection after mitral valve surgery, guiding physicians in taking appropriate preventive measures and diminishing the infection risk.