The aim of this study was to research the expression of IL-37 in systemic lupus erythematosus (SLE) patients and the effect of glucocorticoid on IL-37. Thirty newly diagnosed severe SLE patients receiving prednisone 1 mg/kg/day for 14 consecutive days and 30 healthy subjects were enrolled into this study. The plasma levels of IL-37 and other cytokines were detected by ELISA and the relative mRNA amounts of IL-37 and other cytokines were detected by RT-PCR. The plasma levels of IL-37, IL-18, IL-18BP, IFN-γ, and IL-6 in SLE patients increased significantly compared with healthy controls (p<0.05). The relative amount of IL-37 mRNA increased by 2.45-fold in pre-treatment SLE patients compared with controls (p<0.05). Plasma concentrations of IL-37 correlated with IL-18, IL-18BP, IFN-γ, IL-6 and SLEDAI score in both pre-treatment and post-treatment SLE patients. The plasma levels of IL-37 decreased significantly after treatment of glucocorticoid. The relative amount of IL-37 mRNA decreased by 24.5 % in post-treatment SLE patients compared with pre-treatment ones (p<0.01). In conclusion, IL-37 is upregulated in active SLE patients. IL-37 is correlated with pro-inflammatory cytokines and SLEDAI. Glucocorticoid can downregulate the expression of IL-37 and other cytokines in SLE patients.

Active contour model (ACM) has been a successful method for image segmentation. The existing ACMs poorly segment the images with intensity inhomogeneity or non-homogeneity, and the results highly depend on the initial position of the contour. To overcome these disadvantages, we proposed a fuzzy region-based active contour driven by weighting global and local fitting energy, wherein we propose a fuzzy region energy with local spatial image information, which has been proved convex and ensures the segmentation results independent of initialization, to motivate an initial evolving curve of pseudo level set function (LSF), followed by the pseudo LSF and further smoothed by an edge energy to accurately extract the object boundaries and maintain its distance feature. In addition, in the fuzzy region energy, instead of using the Euler-Lagrange equation to minimize the energy functional, we develop a more direct method to calculate the change of the fuzzy region energy. The experimental results on synthetic and real images with high noise and intensity inhomogeneity show that the proposed model can obtain better performance than the state-of-the-art active contour models, and takes less running time. The code is available at: https://github.com/fangchj2002/FRAGL.

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Active contour models (ACMs) have been widely applied in the field of image segmentation. However, it is still very challenging to construct an efficient ACM to segment images with intensity inhomogeneity. In this paper, a novel ACM guided by global and local signed energy-based pressure force (GLSEPF) is proposed. First, by computing the energy difference between the inner and outer energies of the evolution curve, a global signed energy-based pressure force (GSEPF) is designed, which can improve the robustness to initial curves. Second, a local signed energy-based pressure force (LSEPF) is introduced by computing the pixel-by-pixel energy difference within local neighborhood region, which can handle images with intensity inhomogeneity and noise. Finally, the global image information and the local energy information are used for the global and local force propagation functions, respectively. The global and local variances are used to automatically balance the weights of the GSEPF and the LSEPF, which can solve the problem of setting parameters. Meanwhile, a regularization term and a penalty term are applied to avoid the re-initialization process during iterations and smooth the level set function. Experimental results on different types of images demonstrate that the proposed model is more robust than the popular region-based and mixed ACMs for segmenting images with intensity inhomogeneity and noise.