Aims Naturally secreted extracellular vesicles (EVs) play important roles in stem-mediated cardioprotection. This study aimed to investigate the cardioprotective function and underlying mechanisms of EVs derived from HIF-1α engineered mesenchymal stem cells (MSCs) in a rat model of AMI. Methods and results EVs isolated from HIF-1α engineered MSCs (HIF-1α-EVs) and control MSCs (NC-EVs) were prepared. In in vitro experiments, the EVs were incubated with cardiomyocytes and endothelial cells exposed to hypoxia and serum deprivation (H/SD); in in vivo experiments, the EVs were injected in the acutely infarcted hearts of Sprague-Dawley rats. Compared with NC-EVs, HIF-1α-EVs significantly inhibited the apoptosis of cardiomyocytes and enhanced angiogenesis of endothelial cells; meanwhile, HIF-1α-EVs also significantly shrunk fibrotic area and strengthened cardiac function in infarcted rats. After treatment with EVs/RGD-biotin hydrogels, we observed longer retention, higher stability in HIF-1α-EVs, and stronger cardiac function in the rats. Quantitative real-time PCR (qRT-PCR) displayed that miRNA-221–3p was highly expressed in HIF-1α-EVs. After miR-221–3p was inhibited in HIF-1α-EVs, the biological effects of HIF-1α EVs on apoptosis and angiogenesis were attenuated. Conclusion EVs released by MSCs with HIF-1α overexpression can promote the angiogenesis of endothelial cells and the apoptosis of cardiomyocytes via upregulating the expression of miR-221–3p. RGD hydrogels can enhance the therapeutic efficacy of HIF-1α engineered MSCs-derived EVs.

No abstract

Objective In this study, a risk score for ventricular arrhythmias (VA) were evaluated for predicting the risk of ventricular arrhythmia (VA) of acute myocardial infarction (AMI) patients. Methods Patients with AMI were divided into two sets according to whether VA occurred during hospitalization. Another cohort was enrolled for external validation. The area under the curve (AUC) of receiver operating characteristic (ROC) was calculated to evaluate the accuracy of the model. Results A total of 1493 eligible patients with AMI were enrolled as the training set, of whom 70 (4.7%) developed VA during hospitalization. In-hospital mortality was significantly higher in the VA set than in the non-VA set (31.4% vs 2.7%, P=0.001). The independent predictors of VA in patients with AMI including Killip grade ≥3, STEMI patients, LVEF <50%, frequent premature ventricular beats, serum potassium <3.5 mmol/L, type 2 diabetes, and creatinine level. The AUC of the model for predicting VT/VF in the training set was 0.815 (95% CI: 0.763–0.866). A total of 1149 cases were enrolled from Xuzhou Center Hospital as the external validation set. The AUC of the model in the external validation set for predicting VT/VF was 0.755 (95% CI: 0.687–0.823). Calibration curves indicated a good consistency between the predicted and the observed probabilities of VA in both sets. Conclusion We have established a clinical prediction risk score for predicting the occurrence of VA in AMI patients. The prediction score is easy to use, performs well and can be used to guide clinical practice.

BackgroundPredictive models based on machine learning have been widely used in clinical practice. Patients with acute myocardial infarction (AMI) are prone to the risk of acute kidney injury (AKI), which results in a poor prognosis for the patient. The aim of this study was to develop a machine learning predictive model for the identification of AKI in AMI patients.MethodsPatients with AMI who had been registered in the Medical Information Mart for Intensive Care (MIMIC) III and IV database were enrolled. The primary outcome was the occurrence of AKI during hospitalization. We developed Random Forests (RF) model, Naive Bayes (NB) model, Support Vector Machine (SVM) model, eXtreme Gradient Boosting (xGBoost) model, Decision Trees (DT) model, and Logistic Regression (LR) models with AMI patients in MIMIC-IV database. The importance ranking of all variables was obtained by the SHapley Additive exPlanations (SHAP) method. AMI patients in MIMIC-III databases were used for model evaluation. The area under the receiver operating characteristic curve (AUC) was used to compare the performance of each model.ResultsA total of 3,882 subjects with AMI were enrolled through screening of the MIMIC database, of which 1,098 patients (28.2%) developed AKI. We randomly assigned 70% of the patients in the MIMIC-IV data to the training cohort, which is used to develop models in the training cohort. The remaining 30% is allocated to the testing cohort. Meanwhile, MIMIC-III patient data performs the external validation function of the model. 3,882 patients and 37 predictors were included in the analysis for model construction. The top 5 predictors were serum creatinine, activated partial prothrombin time, blood glucose concentration, platelets, and atrial fibrillation, (SHAP values are 0.670, 0.444, 0.398, 0.389, and 0.381, respectively). In the testing cohort, using top 20 important features, the models of RF, NB, SVM, xGBoost, DT model, and LR obtained AUC of 0.733, 0.739, 0.687, 0.689, 0.663, and 0.677, respectively. Placing RF models of number of different variables on the external validation cohort yielded their AUC of 0.711, 0.754, 0.778, 0.781, and 0.777, respectively.ConclusionMachine learning algorithms, particularly the random forest algorithm, have improved the accuracy of risk stratification for AKI in AMI patients and are applied to accurately identify the risk of AKI in AMI patients.