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To investigate the effects and mechanisms of irisin, a newly discovered myokine, in cartilage development, osteoarthritis (OA) pathophysiology and its therapeutic potential for treating OA we applied the following five strategical analyses using (1) murine joint tissues at different developmental stages; (2) human normal and OA pathological tissue samples; (3) experimental OA mouse model; (4) irisin gene knockout (KO) and knock in (KI) mouse lines and their cartilage cells; (5) in vitro mechanistic experiments. We found that Irisin was involved in all stages of cartilage development. Both human and mouse OA tissues showed a decreased expression of irisin. Intra-articular injection of irisin attenuated ACLT-induced OA progression. Irisin knockout mice developed severe OA while irisin overexpression in both irisin KI mice and intraarticular injection of irisin protein attenuated OA progression. Irisin inhibited inflammation and promoted anabolism in chondrogenic ADTC5 cells. Proliferative potential of primary chondrocytes from KI mice was found to be enhanced, while KO mice showed an inhibition under normal or inflammatory conditions. The primary chondrocytes from irisin KI mice showed reduced expression of inflammatory factors and the chondrocytes isolated from KO mice showed an opposite pattern. In conclusion, it is the first time to show that irisin is involvedmore »
While additive manufacturing (AM) has facilitated the production of complex structures, it has also highlighted the immense challenge inherent in identifying the optimum AM structure for a given application. Numerical methods are important tools for optimization, but experiment remains the gold standard for studying nonlinear, but critical, mechanical properties such as toughness. To address the vastness of AM design space and the need for experiment, we develop a Bayesian experimental autonomous researcher (BEAR) that combines Bayesian optimization and high-throughput automated experimentation. In addition to rapidly performing experiments, the BEAR leverages iterative experimentation by selecting experiments based on all available results. Using the BEAR, we explore the toughness of a parametric family of structures and observe an almost 60-fold reduction in the number of experiments needed to identify high-performing structures relative to a grid-based search. These results show the value of machine learning in experimental fields where data are sparse.