Inflammation and regeneration最新文献

Background: The types of bone damage in rheumatoid arthritis (RA) include joint erosion, periarticular osteoporosis, and systemic osteoporosis. Janus kinase (JAK) inhibitors ameliorate inflammation and joint erosion in RA, but their effect on the three types of bone loss have not been reportedly explored in depth. We aimed to clarify how JAK inhibitors influence the various types of bone loss in arthritis by modulating osteoclastic bone resorption and/or osteoblastic bone formation.

Methods: Collagen-induced arthritis (CIA) mice were treated with a JAK inhibitor after the onset of arthritis. Micro-computed tomography (μCT) and histological analyses (bone morphometric analyses) on the erosive calcaneocuboid joint, periarticular bone (distal femur or proximal tibia), and vertebrae were performed. The effect of four different JAK inhibitors on osteoclastogenesis under various conditions was examined in vitro.

Results: The JAK inhibitor ameliorated joint erosion, periarticular osteopenia and systemic bone loss. It reduced the osteoclast number in all the three types of bone damage. The JAK inhibitor enhanced osteoblastic bone formation in the calcaneus distal to inflammatory synovium in the calcaneocuboid joints, periarticular region of the tibia and vertebrae, but not the inflamed calcaneocuboid joint. All the JAK inhibitors suppressed osteoclastogenesis in vitro to a similar extent in the presence of osteoblastic cells. Most of the JAK inhibitors abrogated the suppressive effect of Th1 cells on osteoclastogenesis by inhibiting IFN-γ signaling in osteoclast precursor cells, while a JAK inhibitor did not affect this effect due to less ability to inhibit IFN-γ signaling.

Conclusions: The JAK inhibitor suppressed joint erosion mainly by inhibiting osteoclastogenesis, while it ameliorated periarticular osteopenia and systemic bone loss by both inhibiting osteoclastogenesis and promoting osteoblastogenesis. These results indicate that the effect of JAK inhibitors on osteoclastogenesis and osteoblastogenesis depends on the bone damage type and the affected bone area. In vitro studies suggest that while JAK inhibitors inhibit osteoclastic bone resorption, their effects on osteoclastogenesis in inflammatory environments vary depending on the cytokine milieu, JAK selectivity and cytokine signaling specificity. The findings reported here should contribute to the strategic use of antirheumatic drugs against structural damages in RA.

Induced pluripotent stem cells (iPSC) are one the most prominent innovations of medical research in the last few decades. iPSCs can be easily generated from human somatic cells and have several potential uses in regenerative medicine, disease modeling, drug screening, and precision medicine. However, further innovation is still required to realize their full potential. Machine learning is an algorithm that learns from large datasets for pattern formation and classification. Deep learning, a form of machine learning, uses a multilayered neural network that mimics human neural circuit structure. Deep neural networks can automatically extract features from an image, although classical machine learning methods still require feature extraction by a human expert. Deep learning technology has developed recently; in particular, the accuracy of an image classification task by using a convolutional neural network (CNN) has exceeded that of humans since 2015. CNN is now used to address several tasks including medical issues. We believe that CNN would also have a great impact on the research of stem cell biology. iPSCs are utilized after their differentiation to specific cells, which are characterized by molecular techniques such as immunostaining or lineage tracing. Each cell shows a characteristic morphology; thus, a morphology-based identification system of cell type by CNN would be an alternative technique. The development of CNN enables the automation of identifying cell types from phase contrast microscope images without molecular labeling, which will be applied to several researches and medical science. Image classification is a strong field among deep learning tasks, and several medical tasks will be solved by deep learning-based programs in the future.