OBJECTIVES: To analyze the association of CHMP2B expression level of with clinicopathological characteristics and prognosis of clear cell renal cell carcinoma (CRCC) and the possible role of CHMP2B in tumorigenesis and progression of CRCC. METHODS: RNAseq data of CRCC were downloaded from the TCGA database for analysis of CHMP2B expression levels in tumor and adjacent tissues and their correlation with clinicopathological characteristics of the patients. Survival outcomes of the patients with high and low CHMP2B expressions were analyzed using the Kaplan-Meier model, and the COX risk regression model was used for identifying the prognostic factors of the patients. The correlation between CHMP2B expression and immune infiltration, its co-expressed genes, and the effect of CHMP2B gene mutations on immunotherapy responses, and its immunohistochemical expression in CRCC and normal tissues were analyzed. Clinical samples of CRCC were collected to examine CHMP2B expressions using RT-PCR, and cell experiment was carried out to test the effect of CHMP2B overexpression on biological behaviors of CRCC cells. RESULTS: CHMP2B was significantly under-expressed in renal cancer tissues, and its overexpression obviously inhibited the proliferation of CRCC cells in vitro. CHMP2B expression level was significantly correlated with age, gender, lymph node metastasis, and tumor stage, and the patients with low CHMP2B expression had poor survival outcomes. Enrichment and co-expression gene analyses suggested that CHMP2B was mainly involved in viral outgrowth, necrotic apoptosis, endocytosis, and immune-regulatory processes in kidney cancer. CONCLUSIONS CHMP2B is lowly expressed in renal cancer tissues to affect tumor progression and tumor immune processes, and may serve as a prognostic biomarker and therapeutic target for CRCC.
Humans ; Carcinoma, Renal Cell/metabolism* ; Kidney Neoplasms/metabolism* ; Cell Proliferation ; Prognosis ; Cell Line, Tumor ; Male ; Female ; Gene Expression Regulation, Neoplastic

This study aims to address the limitations in gesture recognition caused by the susceptibility of temporal and frequency domain feature extraction from surface electromyography signals, as well as the low recognition rates of conventional classifiers. A novel gesture recognition approach was proposed, which transformed surface electromyography signals into grayscale images and employed convolutional neural networks as classifiers. The method began by segmenting the active portions of the surface electromyography signals using an energy threshold approach. Temporal voltage values were then processed through linear scaling and power transformations to generate grayscale images for convolutional neural network input. Subsequently, a multi-view convolutional neural network model was constructed, utilizing asymmetric convolutional kernels of sizes 1 × n and 3 × n within the same layer to enhance the representation capability of surface electromyography signals. Experimental results showed that the proposed method achieved recognition accuracies of 98.11% for 13 gestures and 98.75% for 12 multi-finger movements, significantly outperforming existing machine learning approaches. The proposed gesture recognition method, based on surface electromyography grayscale images and multi-view convolutional neural networks, demonstrates simplicity and efficiency, substantially improving recognition accuracy and exhibiting strong potential for practical applications.
Electromyography/methods* ; Neural Networks, Computer ; Humans ; Gestures ; Signal Processing, Computer-Assisted ; Machine Learning ; Pattern Recognition, Automated/methods* ; Algorithms ; Convolutional Neural Networks