ABSTRACT
Objective To explore the expression of miR-101-3p in gastric cancer and its mechanism on the invasion, metastasis, and angiogenesis of gastric cancer cells by targeting the STC-1 gene to regulate the PI3K/AKT signaling pathway. Methods qRT-PCR was used to detect the expression of miR-101-3p and STC-1 mRNA in gastric cancer tissues and BGC-823 cell and analyze the relationship between miR-101-3p expression and patients' clinical pathological factors. The cells were transfected with miRNA mimics and plasmids separately or in combination with LipofectamineTM 2000. TargetScanHuman prediction and dual-luciferase assay were used to verify the targeted regulation of miR-101-3p on STC-1. The effect and possible mechanism of miR-101-3p targeting the STC-1 gene on the invasion, metastasis, and angiogenesis of cancer cells were verified by scratch test, Transwell chamber test, Matrigel in vitro tube forming test, and Western blot assay. The development of the transplanted tumor was detected by nude mouse tumorigenicity test. Results The expression of STC-1 in gastric cancer tissues was higher than that in normal tissues. Compared with normal gastric tissues and GES-1 cells, miR-101-3p was down-regulated, and STC-1 mRNA was up-regulated in gastric cancer tissues and BGC-823 cell. The level of miR-101-3p was negatively correlated with the level of STC-1, and significantly correlated with the degree of tumor differentiation, TNM stage, and lymph node metastasis (P < 0.05). miR-101-3p directly targeted STC-1. The overexpression of miR-101-3p inhibited STC-1 expression and downregulated the expression of p-PI3K/PI3K, p-AKT/AKT, MMP-2, MMP-9, VEGF, and Ang2, consequently, inhibited tumor cell invasion, metastasis, and angiogenesis and reduced the size and weight of the transplanted tumors (P < 0.05). Conclusion miR-101-3p is down-regulated in gastric cancer and can target the STC-1 gene to regulate the PI3K/AKT signaling pathway and inhibit the invasion, metastasis, and angiogenesis of BGC-823 gastric cancer cells and the development of transplanted tumors in vivo.
ABSTRACT
The automatic recognition technology of muscle fatigue has widespread application in the field of kinesiology and rehabilitation medicine. In this paper, we used surface electromyography (sEMG) to study the recognition of leg muscle fatigue during circuit resistance training. The purpose of this study was to solve the problem that the sEMG signals have a lot of noise interference and the recognition accuracy of the existing muscle fatigue recognition model is not high enough. First, we proposed an improved wavelet threshold function denoising algorithm to denoise the sEMG signal. Then, we build a muscle fatigue state recognition model based on long short-term memory (LSTM), and used the Holdout method to evaluate the performance of the model. Finally, the denoising effect of the improved wavelet threshold function denoising method proposed in this paper was compared with the denoising effect of the traditional wavelet threshold denoising method. We compared the performance of the proposed muscle fatigue recognition model with that of particle swarm optimization support vector machine (PSO-SVM) and convolutional neural network (CNN). The results showed that the new wavelet threshold function had better denoising performance than hard and soft threshold functions. The accuracy of LSTM network model in identifying muscle fatigue was 4.89% and 2.47% higher than that of PSO-SVM and CNN, respectively. The sEMG signal denoising method and muscle fatigue recognition model proposed in this paper have important implications for monitoring muscle fatigue during rehabilitation training and exercise.