Hypoxia-inducible factor-1α (HIF-1α) is a primary metabolic sensor, and is expressed in different immune cells, such as macrophage, dendritic cell, neutrophil, T cell, and non-immune cells, for instance, synovial fibroblast, and islet β cell. HIF-1α signaling regulates cellular metabolism, triggering the release of inflammatory cytokines and inflammatory cells proliferation. It is known that microenvironment hypoxia, vascular proliferation, and impaired immunological balance are present in autoimmune diseases. To date, HIF-1α is recognized to be overexpressed in several inflammatory autoimmune diseases, such as systemic lupus erythematosus (SLE), rheumatoid arthritis, and function of HIF-1α is dysregulated in these diseases. In this review, we narrate the signaling pathway of HIF-1α and the possible immunopathological roles of HIF-1α in autoimmune diseases. The collected information will provide a theoretical basis for the familiarization and development of new clinical trials and treatment based on HIF-1α and inflammatory autoimmune disorders in the future.
ObjectivesTo investigate whether machine learning, which is widely used in disease prediction and diagnosis based on demographic data and serological markers, can predict herpes occurrence in patients with systemic lupus erythematosus (SLE).MethodsA total of 286 SLE patients were included in this study, including 200 SLE patients without herpes and 86 SLE patients with herpes. SLE patients were randomly divided into a training group and a test group, and 18 demographic characteristics and serological indicators were compared between the two groups.ResultsWe selected basophil, monocyte, white blood cell, age, immunoglobulin E, SLE Disease Activity Index, complement 4, neutrophil, and immunoglobulin G as the basic features of modeling. A random forest model had the best performance, but logistic and decision tree analyses had better clinical decision‐making benefits. Random forest had a good consistency between feature importance judgment and feature selection. The 10‐fold cross‐validation showed the optimization of five model parameters.ConclusionThe random forest model may be an excellently performing model, which may help clinicians to identify SLE patients whose disease is complicated by herpes early.
Objective: Systemic lupus erythematosus is a chronic rheumatic disorder. Endothelin-1, a vasoconstrictor, belongs to the endothelin family. To date, association between ET-1 and pathogenesis of SLE remains unclear. Method: This case-control study was carried out by 314 SLE, 252 other inflammatory autoimmune diseases patients and 500 healthy controls. Serum ET-1, CCN3, IL-28B levels were detected by ELISA, and ET-1 gene polymorphisms (rs5369, rs5370, rs1476046, rs2070699, rs2071942, rs2071943, rs3087459, rs4145451, rs6458155, rs9369217) were genotyped with KASP. Results: Raised ET-1 concentrations in SLE patients correlated with clinical characteristics. Serum CCN3, IL-28B expressions were higher in SLE patients, and ET-1 levels were positively correlated with the two cytokines. Rs5370, rs1476046, rs2070699, rs2071942, rs2071943, rs3087459, rs6458155 and rs2070699 were associated with SLE risk. Rs2070699 (T, TT) was related to alopecia. Rs5370 (T, TT, TG), rs1476046 (G,GA), rs2071942 (G,GA) and rs2071943 (G,GA) were associated with pericarditis, pyuria and fever manifestations. Rs3087459 (CC) and rs9369217 (TC) were relevant to anti-SSB indicator. Rs5369 (AA) was associated with IgG and CRP levels. Conclusion: elevated serum ET-1 in SLE patients may be a potential disease marker, and its gene polymorphisms were relevant to SLE susceptibility.
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