首页|Kunming Medical University Reports Findings in Bioinformatics (Investigating the molecular mechanisms between type 1 diabetes and mild cognitive impairment usin g bioinformatics analysis, with a focus on immune response)
Kunming Medical University Reports Findings in Bioinformatics (Investigating the molecular mechanisms between type 1 diabetes and mild cognitive impairment usin g bioinformatics analysis, with a focus on immune response)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Biotechnology - Bioinf ormatics is the subject of a report. According to news reporting from Kunming, P eople’s Republic of China, by NewsRx journalists, research stated, “The immune s ystem is involved in the development and progression of several diseases. Type 1 diabetes mellitus (T1DM), an autoimmune disorder, influences the progression of several other conditions; however, the link between T1DM and mild cognitive imp airment (MCI) remains unclear.” The news correspondents obtained a quote from the research from Kunming Medical University, “This study investigated the underlying immune response mechanisms t hat contribute to the development and progression of T1DM and MCI. Microarray da tasets for MCI (GSE63060) and T1DM (GSE30208) were retrieved from the Gene Expre ssion Omnibus database. Differentially expressed genes (DEGs) were identified us ing the limma package. To explore the functional implications of these DEGs, Gen e Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analys es were conducted using Cluster- Profiler. Protein-protein interaction networks fo r the DEGs were constructed using the STRING database and visualized using Cytos cape. The Molecular Complex Detection algorithm was used to analyze DEGs. Immune cell infiltration in patients with T1DM and MCI was analyzed using the xCell me thod. Gene set enrichment analysis was used to gain in-depth insights into the f unctional characteristics of T1DM and MCI. Immune-related genes were obtained fr om the GeneCards and ImmPort databases. Machine learning algorithms were used to identify potential hub genes associated with immunity for T1DM and MCI diagnosi s, and the diagnostic value was assessed using the receiver operating characteri stic curve.The identified genes were validated using quantitative polymerase ch ain reaction. In the T1DM and MCI datasets, 610 DEGs showed consistent trends, o f which 232 and 378 were upregulated and downregulated, respectively. Immune res ponse analysis revealed significant changes in the immune cells associated with MCI and T1DM. Using immune-related genes, DEGs, and machine learning techniques, we identified CD3D in the MCI and T1DM groups. We observed a gradual decline in the cognitive function of mice with T1DM as the disease progressed. CD3D expres sion increased with increasing disease severity; CD3D primarily affected CD4+ T cells. This study revealed a complex interaction between T1DM and MCI, providing novel insights into the intricate relationship between immune dysregulation and cognitive impairment and expanding our understanding of these two interconnecte d disorders.”
KunmingPeople’s Republic of ChinaAsi aBioinformaticsBiotechnologyCyborgsEmerging TechnologiesGeneticsHeal th and MedicineImmunologyInformation TechnologyInsulin Dependent Diabetes MellitusMachine LearningNutritional and Metabolic Diseases and ConditionsT ype 1 Diabetes
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