BackgroundMesenchymal stem cells (MSCs) have been widely applied to treat various inflammatory diseases. Inflammatory cytokines can induce both apoptosis and autophagy in MSCs. However, whether autophagy plays a pro- or con-apoptosis effect on MSCs in an inflammatory microenvironment has not been clarified.MethodsWe inhibited autophagy by constructing MSCs with lentivirus containing small hairpin RNA to knockdown Beclin-1 and applied these MSCs to a model of sepsis to evaluate therapeutic effect of MSCs.ResultsHere we show that inhibition of autophagy in MSCs increases the survival rate of septic mice more than control MSCs, and autophagy promotes apoptosis of MSCs during application to septic mice. Further study demonstrated that autophagy aggravated tumor necrosis factor alpha plus interferon gamma-induced apoptosis of MSCs. Mechanically, autophagy inhibits the expression of the pro-survival gene Bcl-2 via suppressing reactive oxygen species/mitogen-activated protein kinase 1/3 pathway.ConclusionsOur findings indicate that an inflammatory microenvironment-induced autophagy promotes apoptosis of MSCs. Therefore, modulation of autophagy in MSCs would provide a novel approach to improve MSC survival during immunotherapy.

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Objective This study aims to develop an artificial intelligence‐based method to screen patients with left ventricular ejection fraction (LVEF) of 50% or lesser using electrocardiogram (ECG) data alone. Methods Convolutional neural network (CNN) is a class of deep neural networks, which has been widely used in medical image recognition. We collected standard 12‐lead ECG and transthoracic echocardiogram (TTE) data including the LVEF value. Then, we paired the ECG and TTE data from the same individual. For multiple ECG‐TTE pairs from a single individual, only the earliest data pair was included. All the ECG‐TTE pairs were randomly divided into the training, validation, or testing data set in a ratio of 9:1:1 to create or evaluate the CNN model. Finally, we assessed the screening performance by overall accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Results We retrospectively enrolled a total of 26 786 ECG‐TTE pairs and randomly divided them into training (n = 21 732), validation (n = 2 530), and testing data set (n = 2 530). In the testing set, the CNN algorithm showed an overall accuracy of 73.9%, sensitivity of 69.2%, specificity of 70.5%, positive predictive value of 70.1%, and negative predictive value of 69.9%. Conclusion Our results demonstrate that a well‐trained CNN algorithm may be used as a low‐cost and noninvasive method to identify patients with left ventricular dysfunction.