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Background: Autoimmune disease are fairly common and one that has an excessive degree of disability is Ankylosing spondylitis (AS). As the main cells in connective tissues, fibroblasts may play important roles in AS ossification. The conducted research aims to establish the osteogenic disparity characteristics of fibroblasts cultured in vitro, obtained via AS patients hip joint capsule, as well as investigating the pathological osteogenic molecular workings of AS.Methods: AS patients hip joint capsules were acquired and fracture patients as the control with the finite fibroblast line were established by using tissue culture method. AS fibroblast proliferation, cycle and apoptosis, expression of osteogenic marker genes, osteogenic phenotypes, and the activation degree of the bone morphogenetic protein (BMP)/Smads signalling pathway were detected by flow cytometry, western blotting and real-time fluorescent quantitative polymerase chain reaction.Results: Proliferative activity in AS fibroblasts were abnormally high, and the apoptotic rate decreased.Compared with normal fibroblasts, the mRNA expression of osteogenic marker genes, expression of osteogenic phenotypes, protein expression of core-binding factor a1 (Cbfa1), Smad1, Smad4, Smad5, phosphorylated (p) Smad1, and pSmad5 in AS fibroblasts were higher; however, the expression of Smad6 was lower. Moreover, recombinant human bone morphogenetic protein-2(rhBMP-2) stimulated Cbfa1 expression by normal and AS fibroblasts through the BMP/Smads signalling pathway. Conclusions:The fibroblasts of hip joint capsules in patients with AS cultured in vitro have biologic characteristics of osteogenic differentiation and may be important target cells of AS ossification. The Activated BMP/Smads signalling pathway could potentially be a mechanism relating to fibroblasts differentiating into osteoblasts and an ossification mechanism for AS.

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Recently, a high dimensional classification framework has been proposed to introduce spatial and anatomical priors in classical single kernel support vector machine optimization scheme, wherein the sequential minimal optimization (SMO) training algorithm is adopted, for brain image analysis. However, to satisfy the optimization conditions required in the single kernel case, it is unreasonably assumed that the spatial regularization parameter is equal to the anatomical one. In this letter, this approach is improved by combining SMO algorithm with multiple kernel learning to avoid that assumption and optimally estimate two parameters. The improvement is comparably demonstrated by experimental results on classification of Alzheimer patients and elderly controls.