Downregulation of the CD151 protects the cardiac function by the crosstalk between the endothelial cells and cardiomyocytes via exosomes

by Luying Jiang, Jingbo Liu, Zhenjia Yang, Jianyu Wang, Wenkai Ke, Kaiyue Zhang, Chunran Zhang, Houjuan Zuo

Background

Heart failure (HF) is the last stage in the progression of various cardiovascular diseases. Although it is documented that CD151 contributes to regulate the myocardial infarction, the function of CD151 on HF and involved mechanisms are still unclear.

Method and results

In the present study, we found that the recombinant adeno-associated virus (rAAV)-mediated endothelial cell-specific knockdown of CD151-transfected mice improved transverse aortic constriction (TAC)-induced cardiac function, attenuated myocardial hypertrophy and fibrosis, and increased coronary perfusion, whereas overexpression of the CD151 protein aggravated cardiac dysfunction and showed the opposite effects. In vitro, the cardiomyocytes hypertrophy induced by PE were significantly improved, while the proliferation and migration of cardiac fibroblasts (CFs) were significantly reduced, when co-cultured with the CD151-silenced endothelial cells (ECs). To further explore the mechanisms, the exosomes from the CD151-silenced ECs were taken by cardiomyocyte (CMs) and CFs, verified the intercellular communication. And the protective effects of CD151-silenced ECs were inhibited when exosome inhibitor (GW4869) was added. Additionally, a quantitative proteomics method was used to identify potential proteins in CD151-silenced EC exosomes. We found that the suppression of CD151 could regulate the PPAR signaling pathway via exosomes.

Conclusion

Our observations suggest that the downregulation of CD151 is an important positive regulator of cardiac function of heart failure, which can regulate exosome-stored proteins to play a role in the cellular interaction on the CMs and CFs. Modulating the exosome levels of ECs by reducing CD151 expression may offer novel therapeutic strategies and targets for HF treatment.

Data glove-based gesture recognition using CNN-BiLSTM model with attention mechanism

by Jiawei Wu, Peng Ren, Boming Song, Ran Zhang, Chen Zhao, Xiao Zhang

As a novel form of human machine interaction (HMI), hand gesture recognition (HGR) has garnered extensive attention and research. The majority of HGR studies are based on visual systems, inevitably encountering challenges such as depth and occlusion. On the contrary, data gloves can facilitate data collection with minimal interference in complex environments, thus becoming a research focus in fields such as medical simulation and virtual reality. To explore the application of data gloves in dynamic gesture recognition, this paper proposes a data glove-based dynamic gesture recognition model called the Attention-based CNN-BiLSTM Network (A-CBLN). In A-CBLN, the convolutional neural network (CNN) is employed to capture local features, while the bidirectional long short-term memory (BiLSTM) is used to extract contextual temporal features of gesture data. By utilizing attention mechanisms to allocate weights to gesture features, the model enhances its understanding of different gesture meanings, thereby improving recognition accuracy. We selected seven dynamic gestures as research targets and recruited 32 subjects for participation. Experimental results demonstrate that A-CBLN effectively addresses the challenge of dynamic gesture recognition, outperforming existing models and achieving optimal gesture recognition performance, with the accuracy of 95.05% and precision of 95.43% on the test dataset.