Diabetes mellitus is the leading chronic disease in the world, and diabetic nephropathy (DN) as one of its complications could increase the mortality. The development of DN is associated to abnormal hemodynamic factors like cytokine networks and the intervention of metabolic risk factors like blood pressure, blood glucose, and blood lipid. However, the pathogenesis of DN is still poorly understood. Although glucose-lowering drugs and insulins have significant effects on blood glucose, the fluctuation of blood glucose or other risk factors could continuously damage the kidney. Recent studies reported that the progression of DN is closely related to the expression of long noncoding RNA (lncRNA), which is important for the early diagnosis and targeted intervention of DN. In this review, we briefly summarize the published studies on the functions and potential mechanism of reported lncRNA in the regulation of DN.
Jieduquyuziyin prescription (JP) has been used to treat systemic lupus erythematosus (SLE). Although the effectiveness of JP in the treatment of SLE has been clinically proven, the underlying mechanisms have yet to be completely understood. We observed the therapeutic actions of JP in MRL/lpr mice and their bone marrow-derived macrophages (BMDMs) and the potential mechanism of their inhibition of inflammatory activity. To estimate the effect of JP on suppressing inflammatory activity, BMDMs of MRL/lpr and MRL/MP mice were treated with JP-treated serum, and MRL/lpr mice were treated by JP for 8 weeks. Among them, JP and its treated serum were subjected to quality control, and BMDMs were separated and identified. The results showed that in the JP group of BMDMs stimulated by Lipopolysaccharide (LPS) in MRL/lpr mice, the secretion of interleukin-6 (IL-6) and tumor necrosis factor-a (TNF-a) reduced, and the expressions of Interleukin-1 receptor-associated kinase 1 (IRAK1) and its downstream nuclear factor kB (NF-kB) pathway decreased. Meanwhile, the alleviation of renal pathological damage, the decrease of urinary protein and serum anti-dsDNA contents, the inhibition of TNF-a level, and then the suppression of the IRAK1-NF-kB inflammatory signaling in the spleen and kidney, confirmed that the therapeutic effect of JP. These results demonstrated that JP could inhibit the inflammatory activity of MRL/lpr mice and their BMDMs by suppressing the activation of IRAK1-NF-kB signaling and was supposed to be a good choice for the treatment of SLE.
Image restoration of snow scenes in severe weather is a difficult task. Snow images have complex degradations and are cluttered over clean images, changing the distribution of clean images. The previous methods based on CNNs are challenging to remove perfectly in restoring snow scenes due to their local inductive biases' lack of a specific global modeling ability. In this paper, we apply the vision transformer to the task of snow removal from a single image. Specifically, we propose a parallel network architecture split along the channel, performing local feature refinement and global information modeling separately. We utilize a channel shuffle operation to combine their respective strengths to enhance network performance. Second, we propose the MSP module, which utilizes multi-scale avgpool to aggregate information of different sizes and simultaneously performs multi-scale projection self-attention on multi-head self-attention to improve the representation ability of the model under different scale degradations. Finally, we design a lightweight and simple local capture module, which can refine the local capture capability of the model. In the experimental part, we conduct extensive experiments to demonstrate the superiority of our method. We compared the previous snow removal methods on three snow scene datasets. The experimental results show that our method surpasses the state-of-the-art methods with fewer parameters and computation. We achieve substantial growth by 1.99dB and SSIM 0.03 on the CSD test dataset. On the SRRS and Snow100K datasets, we also increased PSNR by 2.47dB and 1.62dB compared with the Transweather approach and improved by 0.03 in SSIM. In the visual comparison section, our MSP-Former also achieves better visual effects than existing methods, proving the usability of our method.
Figure 1: Realistic winter images (top) from the RWSD dataset created by this work and corresponding restoration results (bottom) using the proposed Degradation-Adaptive Neural Network.
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