This study aims to identify the inhibitory role of the spinal glucagon like peptide-1 receptor (GLP-1R) signaling in pain hypersensitivity and its mechanism of action in rats and mice. First, GLP-1Rs were identified to be specifically expressed on microglial cells in the spinal dorsal horn, and profoundly upregulated after peripheral nerve injury. In addition, intrathecal GLP-1R agonists GLP-1(7-36) and exenatide potently alleviated formalin-, peripheral nerve injury-, bone cancer-, and diabetes-induced hypersensitivity states by 60 -90%, without affecting acute nociceptive responses. The antihypersensitive effects of exenatide and GLP-1 were completely prevented by GLP-1R antagonism and GLP-1R gene knockdown. Furthermore, exenatide evoked ␤-endorphin release from both the spinal cord and cultured microglia. Exenatide antiallodynia was completely prevented by the microglial inhibitor minocycline, ␤-endorphin antiserum, and opioid receptor antagonist naloxone. Our results illustrate a novel spinal dorsal horn microglial GLP-1R/␤-endorphin inhibitory pathway in a variety of pain hypersensitivity states.

This study aims at improving the effectiveness of failure mode and effect analysis (FMEA) technique. FMEA is a widely used technique for identifying and eliminating known or potential failures from system, design, and process. However, in conventional FMEA, risk factors of Severity (S), Occurrence (O), and Detection difficulty (D) are simply multiplied to obtain a crisp risk priority number without considering the subjectivity and vagueness in decision makers' judgments. Besides, the weights for risk factors S, O, and D are also ignored. As a result, the effectiveness and accuracy of the FMEA are affected. To solve this problem, a novel FMEA approach for obtaining a more rational rank of failure modes is proposed. Basically, two stages of evaluation process are described: the determination of risk factors' weights and ranking the risk for the failure modes. A rough group 'Technique for Order Performance by Similarity to Ideal Solution' (TOPSIS) method is used to evaluate the risk of failure mode. The novel approach integrates the strength of rough set theory in handling vagueness and the merit of TOPSIS in modeling multi-criteria decision making. Finally, an application in steam valve system is provided to demonstrate the potential of the methodology under vague and subjective environment.

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