Background Chronic alcohol intake is associated with an increased risk of alcoholic cardiomyopathy, which may present with pathological changes such as myocardial insulin resistance, leading to ventricular dilation and cardiac dysfunction. Although a correlation between microRNA-155 (miR-155) and insulin signaling has been identified, the underlying mechanism has not been elucidated to date. The purpose of the study was to determine whether overexpression of miR-155-5p in vivo could ameliorate chronic alcohol-induced myocardial insulin resistance and cardiac dysfunction. Material and Methods Wistar rats were fed with either alcohol or water for 20 weeks to establish chronic alcohol intakes model. Then the alcohol group were divided into three groups: model group, miRNA-155 group and AAV-NC group. Rats undergoing alcohol treatment were injected with AAV-miRNA-155 (adeno-associated virus 9) or its negative control AAV-NC, respectively. Gene expression was determined by real-time PCR, and protein expression was determined by western blot. Echocardiography was performed to assess terminal cardiac function. Insulin responsiveness was determined through the quantification of phosphorylated insulin receptor substrate 1 (ser 307) and phosphorylated insulin receptor (Tyr 1185) levels. Results We found that cardiac function was attenuated in chronic alcohol intake rats, with an activated mammalian target of rapamycin (mTOR) signaling pathway, accompanied by an increase in p-IRS1(ser 307) and a decrease in p-IR (Tyr 1185) level in myocardial tissue. Also, alcohol drinking significantly up-regulated miR-155-5p level and its overexpression decreased p-IRS1 (ser 307) and increased p-IR (Tyr 1185) levels, and meanwhile inhibited the mTOR signaling pathway. Conclusion miR-155-5p upregulation ameliorates myocardial insulin resistance via the mTOR signaling in chronic alcohol drinking rats. We propose that miR-155 may serve as a novel potential therapeutic target for alcoholic heart disease.

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Action recognition and pose estimation from videos are closely related to understand human motions, but more literature focuses on how to solve pose estimation tasks alone from action recognition. This research shows a faster and more flexible training method for VideoPose3D which is based on action recognition. This model is fed with the same type of action as the type that will be estimated, and different types of actions can be trained separately. Evidence has shown that, for common pose-estimation tasks, this model requires a relatively small amount of data to carry out similar results with the original research, and for action-oriented tasks, it outperforms the original research by 4.5% with a limited receptive field size and training epoch on Velocity Error of MPJPE. This model can handle both action-oriented and common pose-estimation problems.

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