20 research outputs found

    Interpretable machine learning models for classifying low back pain status using functional physiological variables.

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    PURPOSE:To evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting. METHODS:Motion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of > 0.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3. RESULTS:Seven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak [Formula: see text]  = 0.047) in model 1, the deltoid muscle (peak [Formula: see text] =  0.052) in model 2, and the iliocostalis muscle (peak [Formula: see text] =  0.16) in model 3. CONCLUSION:The ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk. These slides can be retrieved under Electronic Supplementary Material

    Effects of selected opioid agonists and antagonists on DMT-and LSD-25-induced disruption of food-rewarded bar pressing behavior in the rat

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    Several opioid agonists and antagonists interact with N,N-dimethyltryptamine (DMT) and lysergic acid diethylamide-25 (LSD) in adult male Holtzman rats trained on a positive reinforcement, fixed ratio 4 (FR 4 ) behavioral schedule, i.e., a reward of 0.01 ml sugar-sweetened milk was earned on every fourth bar press. DMT (3.2 and 10.0 mg/kg) and LSD (0.1 mg/kg) given IP with 0.9% NaCl pretreatment, disrupted food-rewarded FR4 bar pressing. Animals were pretreated IP (10–15 min) with predetermined, behaviorally noneffective doses of morphine, methadone, naltrexone, and the (+)-and (-)-enantiomers of naloxone prior to receiving DMT or LSD. Dose-dependent effects were shown with opioid agonist pretreatment. Morphine (0.32–1.0 mg/kg) and methadone (0.32 mg/kg) significantly antagonized the bar pressing disruption induced by DMT and LSD. Larger doses of morphine (3.2 mg/kg) and methadone (1.0–3.2 mg/kg) potentiated only LSD-induced effects, with no effect on DMT-treated groups. The opioid antagonists (-)-naloxone and naltrexone potentiated the disruption of bar pressing induced by DMT and LSD. Failure of (+)-naloxone to potentiate the DMT effects was attributed to a stereospecific opioid antagonist effect of (-)-naloxone.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46425/1/213_2004_Article_BF00432428.pd
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