The role of high mobility group box chromosomal protein 1 in rheumatoid arthritis

Abstract High mobility group box chromosomal protein 1 (HMGB1) is a ubiquitous highly conserved single polypeptide in all mammal eukaryotic cells. HMGB1 exists mainly within the nucleus and acts as a DNA chaperone. When passively released from necrotic cells or actively secreted into the extracellular milieu in response to appropriate signal stimulation, HMGB1 binds to related cell signal transduction receptors, such as RAGE, TLR2, TLR4 and TLR9, and becomes a proinflammatory cytokine that participates in the development and progression of many diseases, such as arthritis, acute lung injury, graft rejection immune response, ischaemia reperfusion injury and autoimmune liver damage. Only a small amount of HMGB1 release occurs during apoptosis, which undergoes oxidative modification on Cys106 and delivers tolerogenic signals to suppress immune activity. This review focuses on the important role of HMGB1 in the pathogenesis of RA, mainly manifested as the aberrant expression of HMGB1 in the serum, SF and synovial tissues; overexpression of signal transduction receptors; abnormal regulation of osteoclastogenesis and bone remodelling leading to the destruction of cartilage and bones. Intervention with HMGB1 may ameliorate the pathogenic conditions and attenuate disease progression of RA. Therefore administration of an HMGB1 inhibitor may represent a promising clinical approach for the treatment of RA

Support vector machine with quantile hyper-spheres for pattern classification.

This paper formulates a support vector machine with quantile hyper-spheres (QHSVM) for pattern classification. The idea of QHSVM is to build two quantile hyper-spheres with the same center for positive or negative training samples. Every quantile hyper-sphere is constructed by using pinball loss instead of hinge loss, which makes the new classification model be insensitive to noise, especially the feature noise around the decision boundary. Moreover, the robustness and generalization of QHSVM are strengthened through maximizing the margin between two quantile hyper-spheres, maximizing the inner-class clustering of samples and optimizing the independent quadratic programming for a target class. Besides that, this paper proposes a novel local center-based density estimation method. Based on it, ρ-QHSVM with surrounding and clustering samples is given. Under the premise of high accuracy, the execution speed of ρ-QHSVM can be adjusted. The experimental results in artificial, benchmark and strip steel surface defects datasets show that the QHSVM model has distinct advantages in accuracy and the ρ-QHSVM model is fit for large-scale datasets