Objectives: To explore the molecular mechanisms in which vitamin D (VD) regulates T cells, especially Th17 cells in collagen-induced arthritis (CIA).Methods: DBA1/J mice induced for CIA were intraperitoneally treated with VD. CIA clinical symptoms and inflammatory responses including Th1/Th17/Tregs percentages were determined and compared. Mouse naïve CD4+ T cells transduced with miR-124 inhibitor or not were polarized to Th17 cells with or without VD. Subsequently, cellular differentiation and IL-6 signaling moleculars were analyzed.Results: VD treatment significantly delayed CIA onset, decreased incidence and clinical scores of arthritis, downregulated serum IgG levels and ameliorated bone erosion. VD downregulated IL-17A production in CD4+ T cells while increased CD4+Foxp3+Nrp-1+ cells both in draining lymph nodes and synovial fluid in arthritic mice. VD inhibited Th17 cells differentiation in vivo and in vitro and potentially functioning directly on T cells to restrain Th17 cells through limiting IL-6R expression and its downstream signaling including STAT3 phosphorylation, while these effects were blocked when naïve CD4+ T cells were transduced with miR-124 inhibitor.Conclusions: VD treatment ameliorates CIA via suppression of Th17 cells and enhancement of Tregs. miR-124-mediated inhibition of IL-6 signaling, provides a novel explanation for VD's role on T cells in CIA mice or RA patients and suggests that VD may have treatment implications in rheumatoid arthritis.
This work used both experiments and modeling methods to understand some critical issues of Mg discharge behavior for Mg-air primary batteries. The charge transfer resistance is the main internal anodic...
The aim of this work is to demonstrate how a retinal image analysis system, DAPHNE, supports the optimization of diabetic retinopathy (DR) screening programs for grading color fundus photography. Method: Retinal image sets, graded by trained and certified human graders, were acquired from Saudi Arabia, China, and Kenya. Each image was subsequently analyzed by the DAPHNE automated software. The sensitivity, specificity, and positive and negative predictive values for the detection of referable DR or diabetic macular edema were evaluated, taking human grading or clinical assessment outcomes to be the gold standard. The automated software's ability to identify co-pathology and to correctly label DR lesions was also assessed. Results: In all three datasets the agreement between the automated software and human grading was between 0.84 to 0.88. Sensitivity did not vary significantly between populations (94.28%-97.1%) with specificity ranging between 90.33% to 92.12%. There were excellent negative predictive values above 93% in all image sets. The software was able to monitor DR progression between baseline and follow-up images with the changes visualized. No cases of proliferative DR or DME were missed in the referable recommendations. Conclusions: The DAPHNE automated software demonstrated its ability not only to grade images but also to reliably monitor and visualize progression. Therefore it has the potential to assist timely image analysis in patients with diabetes in varied populations and also help to discover subtle signs of sight-threatening disease onset. Translational Relevance: This article takes research on machine vision and evaluates its readiness for clinical use.
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