Loss of Smad7 Promotes Inflammation in Rheumatoid Arthritis

Objective: Smad7 is an inhibitory Smad and plays a protective role in many inflammatory diseases. However, the roles of Smad7 in rheumatoid arthritis (RA) remain unexplored, which were investigated in this study.Methods: The activation of TGF-β/Smad signaling was examined in synovial tissues of patients with RA. The functional roles and mechanisms of Smad7 in RA were determined in a mouse model of collagen-induced arthritis (CIA) in Smad7 wild-type (WT) and knockout (KO) CD-1 mice, a strain resistant to autoimmune arthritis induction.Results: TGF-β/Smad3 signaling was markedly activated in synovial tissues of patients with RA, which was associated with the loss of Smad7, and enhanced Th17 and Th1 immune response. The potential roles of Smad7 in RA were further investigated in a mouse model of CIA in Smad7 WT/KO CD-1 mice. As expected, Smad7-WT CD-1 mice did not develop CIA. Surprisingly, CD-1 mice with Smad7 deficiency developed severe arthritis including severe joint swelling, synovial hyperplasia, cartilage damage, massive infiltration of CD3+ T cells and F4/80+ macrophages, and upregulation of proinflammatory cytokines IL-1β, TNFα, and MCP-1. Further studies revealed that enhanced arthritis in Smad7 KO CD-1 mice was associated with increased Th1, Th2 and, importantly, Th17 over the Treg immune response with overactive TGF-β/Smad3 and proinflammatory IL-6 signaling in the joint tissues.Conclusions: Smad7 deficiency increases the susceptibility to autoimmune arthritis in CD-1 mice. Enhanced TGF-β/Smad3-IL-6 signaling and Th17 immune response may be a mechanism through which disrupted Smad7 causes autoimmune arthritis in CD-1 mice

Preparation of Ce- and La-Doped Li4Ti5O12 Nanosheets and Their Electrochemical Performance in Li Half Cell and Li4Ti5O12/LiFePO4 Full Cell Batteries

This work reports on the synthesis of rare earth-doped Li4Ti5O12 nanosheets with high electrochemical performance as anode material both in Li half and Li4Ti5O12/LiFePO4 full cell batteries. Through the combination of decreasing the particle size and doping by rare earth atoms (Ce and La), Ce and La doped Li4Ti5O12 nanosheets show the excellent electrochemical performance in terms of high specific capacity, good cycling stability and excellent rate performance in half cells. Notably, the Ce-doped Li4Ti5O12 shows good electrochemical performance as anode in a full cell which LiFePO4 was used as cathode. The superior electrochemical performance can be attributed to doping as well as the nanosized particle, which facilitates transportation of the lithium ion and electron transportation. This research shows that the rare earth doped Li4Ti5O12 nanosheets can be suitable as a high rate performance anode material in lithium-ion batteries

A Subject-Sensitive Perceptual Hash Based on MUM-Net for the Integrity Authentication of High Resolution Remote Sensing Images

Data security technology is of great significance to the application of high resolution remote sensing image (HRRS) images. As an important data security technology, perceptual hash overcomes the shortcomings of cryptographic hashing that is not robust and can achieve integrity authentication of HRRS images based on perceptual content. However, the existing perceptual hash does not take into account whether the user focuses on certain types of information of the HRRS image. In this paper, we introduce the concept of subject-sensitive perceptual hash, which can be seen as a special case of conventional perceptual hash, for the integrity authentication of HRRS image. To achieve subject-sensitive perceptual hash, we propose a new deep convolutional neural network architecture, named MUM-Net, for extracting robust features of HRRS images. MUM-Net is the core of perceptual hash algorithm, and it uses focal loss as the loss function to overcome the imbalance between the positive and negative samples in the training samples. The robust features extracted by MUM-Net are further compressed and encoded to obtain the perceptual hash sequence of HRRS image. Experiments show that our algorithm has higher tamper sensitivity to subject-related malicious tampering, and the robustness is improved by about 10% compared to the existing U-net-based algorithm; compared to other deep learning-based algorithms, this algorithm achieves a better balance between robustness and tampering sensitivity, and has better overall performance