ObjectiveTo investigate the therapeutic mechanism of Danhe granules in treating mixed hyperlipidemia based on network pharmacology， as well as animal and cell experiments. MethodsThe active compounds and targets of Danhe granules were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and the Encyclopedia of Traditional Chinese Medicine （ETCM）. Related targets for mixed hyperlipidemia were obtained from the GeneCards database. The intersecting targets were subjected to Gene Ontology （GO） functional annotation and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analyses. A high-fat model was established in human hepatocellular carcinoma cells （HepG2） induced by palmitic acid （PA）， followed by intervention with Danhe granules to assess intracellular lipid accumulation and oxidative stress levels. A mixed hyperlipidemia rat model was also established and divided into low-， medium-， and high-dose Danhe granules groups （1.134， 2.268， and 4.536 g·kg^-1， respectively）， as well as a positive control group treated with pravastatin sodium （4.020 mg·kg^-1）. After eight weeks of intervention， serum lipid levels， inflammatory factors， oxidative stress indices， and the expression of key hepatic lipid metabolism-related proteins were determined. ResultsNetwork pharmacology identified 93 intersecting targets between Danhe granules and mixed hyperlipidemia， with peroxisome proliferator-activated receptor gamma （PPARG）， peroxisome proliferator-activated receptor alpha （PPARA）， tumor necrosis factor （TNF）， interleukin-6 （IL-6）， and IL-1B among the key nodes. The PPAR signaling pathway， AGE/RAGE signaling pathway， lipid metabolism， atherosclerosis and non-alcoholic fatty liver disease （NAFLD） were among the most significantly enriched pathways. Cellular experiments demonstrated that Danhe granules significantly reduced reactive oxygen species （ROS） and malondialdehyde （MDA） levels while increasing catalase （CAT） activity （P<0.05）， thereby alleviating intracellular lipid accumulation and triglyceride （TG） content in HepG2. In animal experiments， Danhe granules markedly decreased serum total cholesterol （TC）， TG， and low-density lipoprotein cholesterol （LDL-C） levels （P<0.05）， reduced hepatic MDA levels， and elevated superoxide dismutase （SOD） and CAT levels. Histological analysis showed alleviation of hepatic steatosis， upregulation of hepatic PPARA and lipoprotein lipase （LPL） expressions， and downregulation of sterol regulatory element-binding protein 1 （SREBP1） expression （P<0.05， P<0.01）. ConclusionDanhe granules improve lipid metabolism disorders in mixed hyperlipidemia by reducing MDA levels， enhancing SOD and CAT activities， scavenging excessive ROS， inhibiting oxidative stress， and mitigating liver injury. The underlying mechanism may involve the upregulation of PPARA and LPL and the suppression of SREBP1 expression.

ObjectiveTo investigate the therapeutic mechanism of Danhe granules in treating mixed hyperlipidemia based on network pharmacology， as well as animal and cell experiments. MethodsThe active compounds and targets of Danhe granules were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform （TCMSP） and the Encyclopedia of Traditional Chinese Medicine （ETCM）. Related targets for mixed hyperlipidemia were obtained from the GeneCards database. The intersecting targets were subjected to Gene Ontology （GO） functional annotation and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analyses. A high-fat model was established in human hepatocellular carcinoma cells （HepG2） induced by palmitic acid （PA）， followed by intervention with Danhe granules to assess intracellular lipid accumulation and oxidative stress levels. A mixed hyperlipidemia rat model was also established and divided into low-， medium-， and high-dose Danhe granules groups （1.134， 2.268， and 4.536 g·kg^-1， respectively）， as well as a positive control group treated with pravastatin sodium （4.020 mg·kg^-1）. After eight weeks of intervention， serum lipid levels， inflammatory factors， oxidative stress indices， and the expression of key hepatic lipid metabolism-related proteins were determined. ResultsNetwork pharmacology identified 93 intersecting targets between Danhe granules and mixed hyperlipidemia， with peroxisome proliferator-activated receptor gamma （PPARG）， peroxisome proliferator-activated receptor alpha （PPARA）， tumor necrosis factor （TNF）， interleukin-6 （IL-6）， and IL-1B among the key nodes. The PPAR signaling pathway， AGE/RAGE signaling pathway， lipid metabolism， atherosclerosis and non-alcoholic fatty liver disease （NAFLD） were among the most significantly enriched pathways. Cellular experiments demonstrated that Danhe granules significantly reduced reactive oxygen species （ROS） and malondialdehyde （MDA） levels while increasing catalase （CAT） activity （P<0.05）， thereby alleviating intracellular lipid accumulation and triglyceride （TG） content in HepG2. In animal experiments， Danhe granules markedly decreased serum total cholesterol （TC）， TG， and low-density lipoprotein cholesterol （LDL-C） levels （P<0.05）， reduced hepatic MDA levels， and elevated superoxide dismutase （SOD） and CAT levels. Histological analysis showed alleviation of hepatic steatosis， upregulation of hepatic PPARA and lipoprotein lipase （LPL） expressions， and downregulation of sterol regulatory element-binding protein 1 （SREBP1） expression （P<0.05， P<0.01）. ConclusionDanhe granules improve lipid metabolism disorders in mixed hyperlipidemia by reducing MDA levels， enhancing SOD and CAT activities， scavenging excessive ROS， inhibiting oxidative stress， and mitigating liver injury. The underlying mechanism may involve the upregulation of PPARA and LPL and the suppression of SREBP1 expression.

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