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find Author "CHEN Wendong" 3 results
  • m6A-related gene clustering analysis and immune cell infiltration analysis in myocardial ischemia-reperfusion injury after cardiopulmonary bypass based on machine learning

    Objective To identify the N6-methyladenosine (m6A)-related characteristic genes analyzed by gene clustering and immune cell infiltration in myocardial ischemia-reperfusion injury (MI/RI) after cardiopulmonary bypass through machine learning. Methods The differential genes associated with m6A methylation were screened by the dataset GSE132176 in GEO, the samples of the dataset were clustered based on the differential gene expression profile, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the differential genes of the m6A cluster after clustering were performed to determine the gene function of the m6A cluster. R software was used to determine the better models in machine learning of support vector machine (SVM) model and random forest (RF) model, which were used to screen m6A-related characteristic genes in MI/RI, and construct characteristic gene nomogram to predict the incidence of disease. R software was used to analyze the correlation between characteristic genes and immune cells, and the online website was used to build a characteristic gene regulatory network. Results In this dataset, a total of 5 m6A-related differential genes were screened, and the gene expression profiles were divided into two clusters for cluster analysis. The enrichment analysis of m6A clusters showed that these genes were mainly involved in regulating monocytes differentiation, response to lipopolysaccharides, response to bacteria-derived molecules, cellular response to decreased oxygen levels, DNA transcription factor binding, DNA-binding transcription activator activity, RNA polymerase Ⅱ specificity, NOD-like receptor signaling pathway, fluid shear stress and atherosclerosis, tumor necrosis factor signaling pathway, interleukin-17 signaling pathway. The RF model was determined by R software as the better model, which determined that METTL3, YTHDF1, RBM15B and METTL14 were characteristic genes of MI/RI, and mast cells, type 1 helper lymphocytes (Th1), type 17 helper lymphocytes (Th17), and macrophages were found to be associated with MI/RI after cardiopulmonary bypass in immune cell infiltration. Conclusion The four characteristic genes METTL3, YTHDF1, RBM15B and METTL14 are obtained by machine learning, while cluster analysis and immune cell infiltration analysis can better reveal the pathophysiological process of MI/RI.

    Release date:2024-09-20 01:01 Export PDF Favorites Scan
  • INFLUENCE ON MATRIX METALLOPROTEINASES 3, 9, AND 13 LEVELS AFTER BLOCKING STROMAL CELL DERIVED FACTOR 1/CHEMOKINE RECEPTOR 4 SIGNALING PATHWAY WITH AMD3100

    Objective To investigate the influence on matrix metalloproteinases (MMP) 3, 9, and 13 levels of human articular cartilage cells after blocking stromal cell derived factor 1 (SDF-1)/ chemokine receptor 4 (CXCR4) signaling pathway withAMD3100 and to define the function mechanism of AMD3100. Methods A total of 144 cartilage blocks from 12 osteoarthritis (OA) patients undergoing total knee arthroplasty (OA cartilage group) and 144 normal cartilage blocks (Mankin score of 0 or 1) from 12 patients undergoing traumatic amputation (normal cartilage group). OA cartilage group was further divided into subgroups A1, B1, and C1, and normal cartilage group into subgroups A2, B2, and C2. The cartilage tissues were cultured in DMEM solution containing 100 ng/mL SDF-1 and 1 000 nmol/L AMD3100 in subgroup A, 100 ng/mL SDF-1 and 1 000 nmol/L MAB310 in subgroup B, and 100 ng/mL SDF-1 in subgroup C, respectively. The levels of MMP-3, 9, and 13 were measured by ELISA; the expressions of MMP-3, 9, and 13mRNA were tested by RT-PCR. Results ELISA and RT-PCR results showed that the levels of MMP-3, 9, and 13 and the expressions of MMP-3, 9, and 13 mRNA were significantly lower in subgroup A than in subgroups B and C at the same time points (P lt; 0.05); the levels of MMP-3, 9, and 13 and the expressions of MMP-3, 9, and 13 mRNA were significantly higher in OA cartilage group than in normal cartilage group at the same time points (P lt; 0.05). Conclusion SDF-1 could induce overexpression and release of MMP-3, 9, and 13 in the articular cartilage through the SDF-1/CXCR4 signaling pathway; AMD3100 could reduce the mRNA expressions and secretion of MMP-3, 9, and 13 in OA cartilage by blocking the SDF-1/CXCR4 signaling pathway; but AMD3100 could not make the secretion of MMP-3, 9, and 13 return to normal levels in OA cartilage.

    Release date:2016-08-31 04:23 Export PDF Favorites Scan
  • A COMPARATIVE STUDY ON THREE-DIMENSIONAL DIGITAL MODEL OF KNEE JOINT ANTERIOR CRUCIATE LIGAMENT BETWEEN NORMAL ADULT AND CORPSE BASED ON MAGNETIC RESONANCE IMAGING RECONSTRUCTION

    Objective To discuss the authenticity of reconstructing the anterior cruciate l igament (ACL) threedimensional digital model of normal adult knee joint by use of MRI. Methods The double knee joint specimens were selected from 20 fresh normal adult corpses and double knee joint of 20 normal adult volunteers, and were scanned with MRI; continuous image data of level thick 1.0 mm were acquired, and then these data were imported into Mimics 10.01 software for three-dimensional reconstruction; and full three-dimensional digital models were built, including the corpse specimens (corpsemodel group) and normal adult (normal model group). The relevance anatomy index of ACL were measured with easuring tool of Mimics 10.01 software, and double knee joint specimens of 20 fresh normal adult corpses were dissected, and the relevance data were measured (corpse specimens group). Results There was no significant difference in all indexes between corpse model group and corpse specimen group (P gt; 0.05), and between corpse model group and normal model group (P gt; 0.05). Conclusion The image data gathered by MRI could reconstruct the ACL three-dimensional digital model of normal adult knee joint, which has authenticity.

    Release date:2016-08-31 05:42 Export PDF Favorites Scan
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