BackgroundMu opioid receptor (MOR) plays a crucial role in mediating analgesic effects of opioids and is closely associated with the pathologies of neuropathic pain. Previous studies have reported that peripheral nerve injury downregulates MOR expression, but the epigenetic mechanisms remain unknown.ObjectiveTherefore, we investigated DNA methyltransferase3a (DNMT3a) expression or methylation changes within MOR promoter in the spinal cord in a neuropathic pain induced by a chronic constriction injury (CCI) mouse model and further determined whether these injury-associated changes are reversible by pharmacological interventions.MethodsA CCI mouse model was established and tissue specimens of lumbar spinal cords were collected. The nociception threshold was evaluated by a Model Heated 400 Base. DNMT3a and MOR mRNA and protein level were detected by real-time-polymerase chain reaction and Western blot, respectively. Methylation of DNMT3a gene was measured by methylation-specific PCR.ResultsOur data showed that chronic nerve injury led to a significant upregulation of DNMT3a expression that was associated with increased methylation of MOR gene promoter and decreased MOR protein expression in the spinal cord. Inhibition of DNMT3a catalytic activity with DNMT inhibitor RG108 significantly blocked the increase in methylation of the MOR promoter, and then upregulated MOR expression and attenuated thermal hyperalgesia in neuropathic pain mice.ConclusionThis study demonstrates that an increase of DNMT3a expression and MOR methylation epigenetically play an important role in neuropathic pain. Targeting DNMT3a to the promoter of MOR gene by DNMT inhibitor may be a promising approach to the development of new neuropathic pain therapy.
Spinal PKCs solely contribute to the initial induction of persistent pain, whereas PKMζ plays an essential role in spinal plasticity storage. PKMζ is responsible for the maintenance of peripheral inflammation-primed PPSP. Therefore, spinal PKMζ may be a therapeutic target to prevent surgery-induced chronic pain in patients with preoperative pain.
A robust topology optimization (RTO) approach with consideration of loading uncertainties is developed in this paper. The stochastic collocation method combined with full tensor product grid and Smolyak sparse grid transforms the robust formulation into a weighted multiple loading deterministic problem at the collocation points. The proposed approach is amenable to implementation in existing commercial topology optimization software package and thus feasible to practical engineering problems. Numerical examples of two-and three-dimensional topology optimization problems are provided to demonstrate the proposed RTO approach and its applications. The optimal topologies obtained from deterministic and robust topology optimization designs under tensor product grid and sparse grid with different levels are compared with one another to investigate the pros and cons of optimization algorithm on final topologies, and an extensive Monte Carlo simulation is also performed to verify the proposed approach.
Background: The exposure of the nucleus pulposus (NP) causes an immune and inflammatory response, which is intrinsically linked to the pathogenesis of radicular pain. As a newly discovered pro-resolving lipid mediator, maresin 1 (MaR1) could exert powerful inflammatory resolution, neuroprotection, and analgesic activities. In the present research, the analgesic effect of MaR1 was observed. Then, the potential mechanism by which MaR1 attenuated radicular pain was also analyzed in a rat model. Methods: Intrathecal administration of MaR1 (10 or 100 ng) was successively performed in a rat with non-compressive lumbar disk herniation for three postoperative days. Mechanical and thermal thresholds were determined to assess pain-related behavior from days 1 to 7 (n = 8/group). On day 7, the tissues of spinal dorsal horns from different groups were gathered to evaluate expression levels of inflammatory cytokines (IL-1β, IL-18, and TNF-α), the NLRP3 inflammasome and pyroptosis indicators (GSDMD, ASC, NLRP3, and Caspase-1), together with NF-κB/p65 activation (n = 6/group). TUNEL and PI staining were performed to further examine the process of pyroptosis. Results: After intrathecal administration in the rat model, MaR1 exhibited potent analgesic effect dose-dependently. MaR1 significantly prompted the resolution of the increased inflammatory cytokine levels, reversed the up-regulated expression of the inflammasome and pyroptosis indicators, and reduced the cell death and the positive activation of NF-κB/p65 resulting from the NP application on the L5 dorsal root ganglion. Conclusion: This study indicated that the activation of NLRP3 inflammasome and pyroptosis played a significant role in the inflammatory reaction of radicular pain. Also, MaR1 could effectively down-regulate the inflammatory response and attenuate pain by inhibiting NLRP3 inflammasome-induced pyroptosis via NF-κB signaling.
A mathematical framework is developed which integrates the reliability concept into topology optimization to solve reliability-based topology optimization (RBTO) problems under uncertainty. Two typical methodologies have been presented and implemented, including the performance measure approach (PMA) and the sequential optimization and reliability assessment (SORA). To enhance the computational efficiency of reliability analysis, stochastic response surface method (SRSM) is applied to approximate the true limit state function with respect to the normalized random variables, combined with the reasonable design of experiments generated by sparse grid design, which was proven to be an effective and special discretization technique. The uncertainties such as material property and external loads are considered on three numerical examples: a cantilever beam, a loaded knee structure, and a heat conduction problem. Monte-Carlo simulations are also performed to verify the accuracy of the failure probabilities computed by the proposed approach. Based on the results, it is demonstrated that application of SRSM with SGD can produce an efficient reliability analysis in RBTO which enables a more reliable design than that obtained by DTO. It is also found that, under identical accuracy, SORA is superior to PMA in view of computational efficiency.
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