Articles: opioid.
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Visual exposure to dim, green, light has been found to reduce pain levels in patients living with migraine, low back pain, and fibromyalgia. Preclinical studies discovered that the analgesic effect of green light was due to the central release of endogenous opioids and a reduction in inflammatory cytokines in the cerebrospinal fluid. The present study assessed the effect of green light therapy (GLT) on joint pain in a rat model of osteoarthritis (OA) and investigated the role of endolipids. ⋯ Serum lipidomics indicated an increase in circulating analgesic endolipids in response to GLT, particularly the N-acyl-glycines. Partial blockade of the endocannabinoid system with the G protein receptor-18/cannabinoid-1 receptor antagonist AM281 (500 μg/kg i.p.) attenuated GLT-induced analgesia. These data show for the first time that GLT acts to reduce OA pain by upregulating circulating analgesic endolipids, which then engage the endocannabinoid system.
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In 2016, Oregon developed an innovative policy to improve care for Medicaid patients with back pain. The objective of this study was to identify the factors associated with dose reduction and discontinuation among Medicaid patients using chronic opioid therapy after implementation of this policy. ⋯ Most Medicaid beneficiaries had a dose reduction after implementation of Oregon's back pain policy. Opioid discontinuation was associated with factors that suggest that providers pursue this strategy for patients at higher overdose risk.
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Anesthesia and analgesia · Oct 2024
Developing a Wearable Sensor-Based Digital Biomarker of Opioid Dependence.
Repeated opioid exposure leads to a variety of physiologic adaptations that develop at different rates and may foreshadow risk of opioid-use disorder (OUD), including dependence and withdrawal. Digital pharmacovigilance strategies that use noninvasive sensors to identify physiologic adaptations to opioid use represent a novel strategy to facilitate safer opioid prescribing. This study aims to identify wearable sensor-derived features associated with opioid dependence by comparing opioid-naïve individuals to chronic opioid users with acute pain and developing a machine-learning model to distinguish between the 2 groups. ⋯ Wearable sensor-derived digital biomarkers can be used to predict opioid use status (naïve versus chronic) and the differentiating features may be detecting opioid dependence. Future work should be aimed at further delineating the phenomenon identified in these models (including opioid dependence and/or withdrawal) and at identifying transition states where an individual changes from 1 profile to another with repetitive opioid exposure.