12 research outputs found

    Toward Composite Pain Biomarkers of Neuropathic Pain—Focus on Peripheral Neuropathic Pain

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    Chronic pain affects ~10–20% of the U.S. population with an estimated annual cost of $600 billion, the most significant economic cost of any disease to-date. Neuropathic pain is a type of chronic pain that is particularly difficult to manage and leads to significant disability and poor quality of life. Pain biomarkers offer the possibility to develop objective pain-related indicators that may help diagnose, treat, and improve the understanding of neuropathic pain pathophysiology. We review neuropathic pain mechanisms related to opiates, inflammation, and endocannabinoids with the objective of identifying composite biomarkers of neuropathic pain. In the literature, pain biomarkers typically are divided into physiological non-imaging pain biomarkers and brain imaging pain biomarkers. We review both types of biomarker types with the goal of identifying composite pain biomarkers that may improve recognition and treatment of neuropathic pain

    A high-frequency mobile phone data collection approach for research in social-environmental systems: Applications in climate variability and food security in sub-Saharan Africa

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    Collecting high-frequency social-environmental data about farming practices in sub-Saharan Africa can provide new insight into environmental changes that farmers face and how they respond within smallholder agro-eco-systems. Traditional data collection methods such as agricultural censuses are costly and not useful for understanding intra-annual and real-time decisions. Short-message service (SMS) has the potential to transform the nature of data collection in coupled social-ecological systems. We present a system for collecting, managing, and synthesizing weekly data from farmers, including data infrastructure for management of big and heterogeneous datasets; probabilistic data quality assessment tools; and visualization and analysis tools such as mapping and regression techniques. We discuss limitations of collecting social-environmental data via SMS and data integration challenges that arise when linking these data with other social and environmental data. In combination with high-frequency environmental data, such data will help ameliorate issues of scale mismatch and build resilience in environmental systems.National Science Foundation [SES-1360463, SES-1534544, BCS-1115009]; NASA New Investigator Program [NNX15AC64G]24 month embargo; published online: 20 May 2019This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

    Whole-Body Vibration Prevents Neuronal, Neurochemical, and Behavioral Effects of Morphine Withdrawal in a Rat Model

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    Peripheral mechanoreceptor-based treatments such as acupuncture and chiropractic manipulation have shown success in modulating the mesolimbic dopamine (DA) system originating in the ventral tegmental area (VTA) of the midbrain and projecting to the nucleus accumbens (NAc) of the striatum. We have previously shown that mechanoreceptor activation via whole-body vibration (WBV) ameliorates neuronal and behavioral effects of chronic ethanol exposure. In this study, we employ a similar paradigm to assess the efficacy of WBV as a preventative measure of neuronal and behavioral effects of morphine withdrawal in a Wistar rat model. We demonstrate that concurrent administration of WBV at 80 Hz with morphine over a 5-day period significantly reduced adaptations in VTA GABA neuronal activity and NAc DA release and modulated expression of δ-opioid receptors (DORs) on NAc cholinergic interneurons (CINs) during withdrawal. We also observed a reduction in behavior typically associated with opioid withdrawal. WBV represents a promising adjunct to current intervention for opioid use disorder (OUD) and should be examined translationally in humans
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