首页|Fourth Affiliated Hospital of Harbin Medical University Reports Findings in Non- Alcoholic Fatty Liver Disease (Identification of neutrophil extracellular trap-r elated biomarkers in non-alcoholic fatty liver disease through machine learning and ...)
Fourth Affiliated Hospital of Harbin Medical University Reports Findings in Non- Alcoholic Fatty Liver Disease (Identification of neutrophil extracellular trap-r elated biomarkers in non-alcoholic fatty liver disease through machine learning and ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Liver Diseases and Con ditions - Non-Alcoholic Fatty Liver Disease is the subject of a report. Accordin g to news originating from Harbin, People’s Republic of China, by NewsRx corresp ondents, research stated, “Non-alcoholic Fatty Liver Disease (NAFLD), noted for its widespread prevalence among adults, has become the leading chronic liver con dition globally. Simultaneously, the annual disease burden, particularly liver c irrhosis caused by NAFLD, has increased significantly.” Our news journalists obtained a quote from the research from the Fourth Affiliat ed Hospital of Harbin Medical University, “Neutrophil Extracellular Traps (NETs) play a crucial role in the progression of this disease and are key to the patho genesis of NAFLD. However, research into the specific roles of NETs-related gene s in NAFLD is still a field requiring thorough investigation. Utilizing techniqu es like AddModuleScore, ssGSEA, and WGCNA, our team conducted gene screening to identify the genes linked to NETs in both single-cell and bulk transcriptomics. Using algorithms including Random Forest, Support Vector Machine, Least Absolute Shrinkage, and Selection Operator, we identified ZFP36L2 and PHLDA1 as key hub genes. The pivotal role of these genes in NAFLD diagnosis was confirmed using th e training dataset GSE164760. This study identified 116 genes linked to NETs acr oss single-cell and bulk transcriptomic analyses. These genes demonstrated enric hment in immune and metabolic pathways. Additionally, two NETs-related hub genes , PHLDA1 and ZFP36L2, were selected through machine learning for integration int o a prognostic model. These hub genes play roles in inflammatory and metabolic p rocesses. scRNA-seq results showed variations in cellular communication among ce lls with different expression patterns of these key genes.”
HarbinPeople’s Republic of ChinaAsiaAlcohol-Induced Diseases and ConditionsAlcoholic Fatty LiverAlcoholismBi omarkersBlood CellsCyborgsDiagnostics and ScreeningDigestive System Dise ases and ConditionsEmerging TechnologiesFatty LiverFatty Liver DiseaseGe neticsGranulocytesHealth and MedicineHemic and Immune SystemsImmunologyLiver Diseases and ConditionsMachine LearningNeutrophilsNon-Alcoholic Fat ty Liver DiseasePhagocytes
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