Objective. Since noise is inevitably introduced during the measurement process of surface electromyographic (sEMG) signals, two novel methods for denoising based on the variational mode decomposition (VMD) method were proposed in this work. Prior to this study, there has been no literature relating to how VMD is applied to sEMG denoising. Approach. The first proposed method uses the VMD method to decompose the signal into multiple variational mode functions (VMFs), each of which has its own center frequency and narrow band, and then the wavelet soft thresholding (WST) method is applied to each VMF. This method is termed the VMD-WST. The second proposed method uses the VMD method to decompose the signal into multiple VMFs, and then the soft interval thresholding (SIT) method is performed on each VMF, which is abbreviated as VMD-SIT. Ten healthy subjects and ten stroke patients participated in the experiment, and the sEMG signals of bicep brachii were measured and analyzed. In this paper, three methods are used for quantitative evaluation of the filtering performance: the signal-to-noise ratio (SNR), root mean square error and R-squared value. The proposed two methods (VMD-WST, VMD-SIT) are compared with the empirical mode decomposition (EMD) method and the wavelet method. Main results. The experimental results showed that the VMD-WST and VMD-SIT methods can effectively filter the noise effect, and the denoising effects were better than the EMD method and the wavelet method. The VMD-SIT method has the best performance. Significance. This study provides a new means of eliminating the noise of sEMG signals based on the VMD method, and it can be applied in the fields of limb movement classification, disease diagnosis, human-machine interaction and so on.
Critical limb ischemia (CLI) is the most severe clinical manifestation of peripheral arterial disease, which causes many amputations and deaths. Conventional treatment strategies for CLI (e.g., stent implantation and vascular surgery) bring surgical risk, which are not suitable for each patient. Extracellular vesicles (EVs) can be a potential solution for CLI. Herein, vascular endothelial growth factor (VEGF; i.e., a crucial molecule related to angiogenesis) and transcription factor EB (TFEB; i.e., a pivotal regulator of autophagy) are chosen as the target gene to improve the bioactivity of EVs derived from endothelial cells. The VEGF/TFEB-engineered EVs (Engineered-EVs) are fabricated by genetically engineering the parent cells, and their versatile functions are confirmed using three cell models (human umbilical vein endothelial cells, myoblast, and monocytes). Injectable thermal-responsive hydrogel are then combined with Engineered-EVs to combat CLI. These results reveal that the hydrogel can enhance the stability of Engineered-EVs in vivo and release EVs at different temperatures. Moreover, the results of animal studies indicate that Engineered-EV/Hydrogel can significantly improve neovascularization, attenuate muscle injury, and recover limb function after CLI. Finally, mechanistic studies shed light on the therapeutic effect of Engineered-EV/Hydrogel due to the activated VEGF/VEGFR pathway and autophagy-lysosomal pathway.
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