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We have been trying to find a miRNA that can specifically regulate the function of mycobacterial host cells to achieve the purpose of eliminating Mycobacterium tuberculosis. The purpose of this study is to investigate the regulation of mmu-let-7a-5p on macrophages apoptosis and its effect on intracellular BCG clearance. After a series of in vitro experiments, we found that mmu-let-7a-5p could negatively regulate the apoptosis of macrophages by targeting Caspase-3. The extrinsic apoptosis signal axis TNFR1/FADD/Caspase-8/Caspase-3 was inhibited after BCG infection. Up-regulated the expression level of mmu-let-7a-5p increase the cell proliferation viability and inhibit apoptosis rate of macrophages, but down-regulated its level could apparently reduce the bacterial load of intracellular Mycobacteria and accelerate the clearance of residual Mycobacteria effectively. Mmu-let-7a-5p has great potential to be utilized as an optimal candidate exosomal loaded miRNA for anti-tuberculosis immunotherapy in our subsequent research. [ABSTRACT FROM AUTHOR]

Magnitude is one of the fundamental parameters of earthquake and also one of the essential information for earthquake early warning (EEW). Rapidly and accurately determining the high-or low-magnitude event is important for mitigating the damage of earthquake hazard. To address the problem of predicting whether an earthquake event is high magnitude ($M \ge5.5$) or low magnitude ($M < 5.5$), this letter proposes a machine learning magnitude classification framework (MCFrame) using single station data, which consists of feature extraction module and magnitude classifier, and we train the feature extraction module of MCFrame to learn the characteristics of high-magnitude ($M \ge5.5$) and low-magnitude ($M < 5.5$) earthquake events, using strong-motion records collected from the Japanese Kyoshin network (K-NET) seismic network. Then, the extracted features are used as an input of magnitude classifier to classify earthquake magnitudes. Meanwhile, we analyze the impact of three different magnitude classifiers on the performance of MCFrame, which include the deep neural network (DNN), support vector machine (SVM), and random forest (RF). We show that within 5 s after the P-wave arrival, for these three different classifiers, the magnitude classification accuracy of MCFrame is close, and the MCFrame proposed in this work has a better performance than baseline models for magnitude classification. Additionally, for the MCFrame, the accuracy of high-magnitude events ($M \ge5.5$) is more than 90%, and the accuracy of low-magnitude events ($M < 5.5$) is more than 99%. These results indicate that the MCFrame proposed in this work is significant for EEW. [ABSTRACT FROM AUTHOR]