Advances in cellular and molecular pathways of salivary gland damage in Sjögren's syndrome.

Sjögren's Syndrome (SS) is an autoimmune disorder characterized by dysfunction of exocrine glands. Primarily affected are the salivary glands, which exhibit the most frequent pathological changes. The pathogenesis involves susceptibility genes, non-genetic factors such as infections, immune cells-including T and B cells, macrophage, dendritic cells, and salivary gland epithelial cells. Inflammatory mediators such as autoantibodies, cytokines, and chemokines also play a critical role. Key signaling pathways activated include IFN, TLR, BAFF/BAFF-R, PI3K/Akt/mTOR, among others. Comprehensive understanding of these mechanisms is crucial for developing targeted therapeutic interventions. Thus, this study explores the cellular and molecular mechanisms underlying SS-related salivary gland damage, aiming to propose novel targeted therapeutic approaches.

STaRNet: A spatio-temporal and Riemannian network for high-performance motor imagery decoding.

Brain-computer interfaces (BCIs), representing a transformative form of human-computer interaction, empower users to interact directly with external environments through brain signals. In response to the demands for high accuracy, robustness, and end-to-end capabilities within BCIs based on motor imagery (MI), this paper introduces STaRNet, a novel model that integrates multi-scale spatio-temporal convolutional neural networks (CNNs) with Riemannian geometry. Initially, STaRNet integrates a multi-scale spatio-temporal feature extraction module that captures both global and local features, facilitating the construction of Riemannian manifolds from these comprehensive spatio-temporal features. Subsequently, a matrix logarithm operation transforms the manifold-based features into the tangent space, followed by a dense layer for classification. Without preprocessing, STaRNet surpasses state-of-the-art (SOTA) models by achieving an average decoding accuracy of 83.29% and a kappa value of 0.777 on the BCI Competition IV 2a dataset, and 95.45% accuracy with a kappa value of 0.939 on the High Gamma Dataset. Additionally, a comparative analysis between STaRNet and several SOTA models, focusing on the most challenging subjects from both datasets, highlights exceptional robustness of STaRNet. Finally, the visualizations of learned frequency bands demonstrate that temporal convolutions have learned MI-related frequency bands, and the t-SNE analyses of features across multiple layers of STaRNet exhibit strong feature extraction capabilities. We believe that the accurate, robust, and end-to-end capabilities of the STaRNet will facilitate the advancement of BCIs.

BrainGridNet: A two-branch depthwise CNN for decoding EEG-based multi-class motor imagery.

Brain-computer interfaces (BCIs) based on motor imagery (MI) enable the disabled to interact with the world through brain signals. To meet demands of real-time, stable, and diverse interactions, it is crucial to develop lightweight networks that can accurately and reliably decode multi-class MI tasks. In this paper, we introduce BrainGridNet, a convolutional neural network (CNN) framework that integrates two intersecting depthwise CNN branches with 3D electroencephalography (EEG) data to decode a five-class MI task. The BrainGridNet attains competitive results in both the time and frequency domains, with superior performance in the frequency domain. As a result, an accuracy of 80.26 percent and a kappa value of 0.753 are achieved by BrainGridNet, surpassing the state-of-the-art (SOTA) model. Additionally, BrainGridNet shows optimal computational efficiency, excels in decoding the most challenging subject, and maintains robust accuracy despite the random loss of 16 electrode signals. Finally, the visualizations demonstrate that BrainGridNet learns discriminative features and identifies critical brain regions and frequency bands corresponding to each MI class. The convergence of BrainGridNet's strong feature extraction capability, high decoding accuracy, steady decoding efficacy, and low computational costs renders it an appealing choice for facilitating the development of BCIs.

The effects of mesenchymal stem cells-derived exosomes in rheumatoid arthritis: a review.

Rheumatoid arthritis (RA) is a most common chronic joint disease belonging to inflammatory autoimmune disease. The pathology of the disease is characterised by the infiltration and proliferation of fibroblast like synoviocytes (FLSs) and the destruction of the bone and cartilage matrix, which leads to joint dysfunction and even deformity.In recent years, an increasing number of studies have shown that MSCs have immunosuppressive properties and have been demonstrated in a variety of disease. Exosomes serve as carriers that mediate intercellular material transfer and information exchange and contain a variety of biologically active components such as proteins, lipids, and nucleic acids. Mesenchymal stem cell-derived exosomes (MSCs-Exos) play a regulatory role by carrying bioactive substances from the parental cells. Exos-derived from MSCs of different origins can modulate several pathological processes, such as immune inflammatory response, improvement of bone metabolism. In this research, we reviewed the current major pathogenesis of RA and explored the important role of MSCs-Exos in this disease. To be more precise, we summarised the effects of different MSCs-Exos on the pathomechanisms of RA, with a view to providing guidance and reference for future studies.

Possible Future Avenues for Rheumatoid Arthritis Therapeutics: Hippo Pathway.

Rheumatoid arthritis (RA) is a persistent systemic autoimmune disease with the hallmarks of swelling of the joint, joint tenderness, and progressive joint destruction, which may cause synovial inflammation and pannus as a basic pathological change, resulting in joint malformations and serious disorders. At present, the precise etiology and mechanism of pathogenesis of RA are unknown. The imbalance of immune homeostasis is the origin of RA. Hippo pathway is widely expressed in a range of cell lineages and plays a fundamental role in maintaining the immune steady state and may be involved in the pathogenic mechanism of RA. This study reviews the progress of Hippo pathway and its main members in the pathogenesis of RA from three aspects: regulating the maintenance of autoimmune homeostasis, promoting the pathogenicity of synovial fibroblasts and regulating the differentiation of osteoclasts. The study also presents a new way to recognize the pathogenesis of rheumatoid arthritis, which is favorable for finding a new way for treating the rheumatoid arthritis.

The role, targets and mechanisms of traditional Chinese medicine in regulating the balance of T helper type 17/regulatory Tcells in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a persistent systemic autoimmune disease, having all the hallmarks of joint swelling, joint tenderness, and progressive joint destruction, with synovitis and pannus formation as the basic pathological changes. T-lymphocyte infiltration is the key to its pathogenesis. During the growth of RA, the share of regulatory T (Treg) cells decreases, while the percentage of T helper type 17 (Th17) cells increases, giving rise to an imbalance of Th17/Treg cells. Modern medicine has made great advances in the treatment of RA and the selection of available drugs, but there are also the disadvantages of gastrointestinal reaction, high price, and low patient compliance. Therapy of RA remains a problem. Traditional Chinese medicine (TCM) has RA therapy developments, both in experimental research and clinical research, and its advantages of lasting effects and less detrimental reactions and fewer adverse effects are accepted by most patients. Numerous clinical and experimental studies have been performed in TCM on regulating Th17/Treg balance. However, the detailed mechanism of TCM intervention in Th17/Treg equilibrium in preventing and treating RA has not been discovered. In this article, the theory of regulating Th17/Treg cell equilibrium in RA is described from the perspectives of single Chinese medicine, active components of Chinese medicine, Chinese medicine compounds, and other therapies of TCM. It was found that TCM can regulate Th17/Treg cell balance and inhibit immunoreaction by intervening in cytokines, transcription factors, and signal pathways. It enables us to comprehensively and deeply understand the mechanism of TCM intervening in Th17/Treg balance in RA; provides direction for clinical therapy of RA; and offers new thoughts for understanding the pathogenesis of RA.