Neuropathic pain (NP) is a clinically incurable disease with miscellaneous causes, complicated mechanisms and available therapies show poor curative effect. Some recent studies have indicated that neuroinflammation plays a vital role in the occurrence and promotion of NP and anti-inflammatory therapy has the potential to relieve the pain. During the past decades, mesenchymal stem cells (MSCs) with properties of multipotentiality, low immunogenicity and anti-inflammatory activity have showed excellent therapeutic effects in cell therapy from animal models to clinical application, thus aroused great attention. However there are no reports about the effect of intrathecal human umbilical cord-derived mesenchymal stem cells (HUC-MSCs) on NP which is induced by peripheral nerve injury. Therefore, in this study, intrathecally transplanted HUC-MSCs were utilized to examine the effect on neuropathic pain induced by a rat model with spinal nerve ligation (SNL), so as to explore the possible mechanism of those effects. As shown in the results, the HUC-MSCs transplantation obviously ameliorated SNL-induced mechanical allodynia and thermal hyperalgesia, which was related to the inhibiting process of neuroinflammation, including the suppression of activated astrocytes and microglia, as well as the significant reduction of pro-inflammatory cytokines Interleukin-1β (IL-1β) and Interleukin -17A (IL-17A) and the up-regulation of anti-inflammatory cytokine Interleukin -10 (IL-10). Therefore, through the effect on glial cells, pro-inflammatory and anti-inflammatory cytokine, the targeting intrathecal HUC-MSCs may offer a novel treatment strategy for NP.
This paper presents a complete method to carry out a fuzzy study of a friction-induced vibration system and to analyze the effects of uncertainty on the output data of a stability problem. The proposed approach decomposes the fuzzy problem into interval problems and calculates interval output solutions by optimization. Next, each calculation of the stability problem output data, which is useful during the optimization process, is reanalyzed by integrating fuzzy logic controllers for the static step and homotopy development and projection techniques for the modal step. Finally, the obtained results are compared with Zadeh’s extension principle reference.
This research presents an optimal trajectory tracking control method for improving the accuracy of 3-DOF translational parallel robots in the surgery field based on the Particle Swarm Optimization (PSO) method. The 3-DOF translational robot has three translational degrees of freedom, which consists of three arms with three revolute joints and twelve spherical joints. Firstly, the kinematics model is established; and the dynamics equation of the Robot is built by applying the Lagrange equation of the first type, and then the dynamics controller of 3-DOF translational robot is designed base on the dynamics equations. Secondly, a trajectory tracking controller model using the Particle Swarm Optimization based on inverse dynamics controlled method for 3-DOF translational Robot is designed. The control performance results of the proposed controller is evaluated by simulation and compared with the other published research results. Finally, the proposed controller can achieve better tracking performance in comparison with other controllers as Proportional Integral Derivative (PID) controller, GPI controller, and adaptive controller.
This paper is devoted to study the generalized synchronization of spatial chaotic systems by applying linear coupling. Based on the stability of the fixed point of a plane system, we obtain the stable domain of the space plane. According to the stable domain of the space plane, the stable domain of the coupling strength for the linear generalized synchronization of the spatial chaotic systems is determined. Moreover, the relationship between the stable fixed plane and the synchronization of the spatial chaos system is also analyzed. Finally, an example is presented to validate the scheme and the analysis.
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