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BackgroundChronic pain imposes a large burden on individuals and society. A patient-centric digital chronic pain management app called Manage My Pain (MMP) can be used to enhance communication between providers and patients and promote self-management. ObjectiveThe purpose of this study was to evaluate the real-world engagement of patients in urban and rural settings in Ontario, Canada with the MMP app alongside their standard of care and assess the impact of its usage on clinical outcomes of pain and related mental health. MethodsA total of 246 participants with chronic pain at a rural and 2 urban pain clinics were recruited into this prospective, open-label, exploratory study that compared the use of MMP, a digital health app for pain that incorporates validated questionnaires and provides patients with summarized reports of their progress in combination with standard care (app group), against data entered on paper-based questionnaires (nonapp group). Participants completed validated questionnaires on anxiety, depression, pain catastrophizing, satisfaction, and daily opioid consumption up to 4.5 months after the initial visit (short-term follow-up) and between 4.5 and 7 months after the initial visit (long-term follow-up). Engagement and clinical outcomes were compared between participants in the two groups. ResultsA total of 73.6% (181/246) of the participants agreed to use the app, with 63.4% (111/175) of them using it for at least one month. Individuals who used the app rated lower anxiety (reduction in Generalized Anxiety Disorder 7-item questionnaire score by 2.10 points, 95% CI –3.96 to –0.24) at short-term follow-up and had a greater reduction in pain catastrophizing (reduction in Pain Catastrophizing Scale score by 5.23 points, 95% CI –9.55 to –0.91) at long-term follow-up relative to patients with pain who did not engage with the MMP app. ConclusionsThe use of MMP by patients with chronic pain is associated with engagement and improvements in self-reported anxiety and pain catastrophizing. Further research is required to understand factors that impact continued engagement and clinical outcomes in patients with chronic pain. Trial RegistrationClinicalTrials.gov NCT04762329; https://clinicaltrials.gov/ct2/show/NCT04762329

BackgroundPain volatility is an important factor in chronic pain experience and adaptation. Previously, we employed machine-learning methods to define and predict pain volatility levels from users of the Manage My Pain app. Reducing the number of features is important to help increase interpretability of such prediction models. Prediction results also need to be consolidated from multiple random subsamples to address the class imbalance issue. ObjectiveThis study aimed to: (1) increase the interpretability of previously developed pain volatility models by identifying the most important features that distinguish high from low volatility users; and (2) consolidate prediction results from models derived from multiple random subsamples while addressing the class imbalance issue. MethodsA total of 132 features were extracted from the first month of app use to develop machine learning–based models for predicting pain volatility at the sixth month of app use. Three feature selection methods were applied to identify features that were significantly better predictors than other members of the large features set used for developing the prediction models: (1) Gini impurity criterion; (2) information gain criterion; and (3) Boruta. We then combined the three groups of important features determined by these algorithms to produce the final list of important features. Three machine learning methods were then employed to conduct prediction experiments using the selected important features: (1) logistic regression with ridge estimators; (2) logistic regression with least absolute shrinkage and selection operator; and (3) random forests. Multiple random under-sampling of the majority class was conducted to address class imbalance in the dataset. Subsequently, a majority voting approach was employed to consolidate prediction results from these multiple subsamples. The total number of users included in this study was 879, with a total number of 391,255 pain records. ResultsA threshold of 1.6 was established using clustering methods to differentiate between 2 classes: low volatility (n=694) and high volatility (n=185). The overall prediction accuracy is approximately 70% for both random forests and logistic regression models when using 132 features. Overall, 9 important features were identified using 3 feature selection methods. Of these 9 features, 2 are from the app use category and the other 7 are related to pain statistics. After consolidating models that were developed using random subsamples by majority voting, logistic regression models performed equally well using 132 or 9 features. Random forests performed better than logistic regression methods in predicting the high volatility class. The consolidated accuracy of random forests does not drop significantly (601/879; 68.4% vs 618/879; 70.3%) when only 9 important features are included in the prediction model. ConclusionsWe employed feature selection methods to identify important features in predicting future pain volatility. To address class imbalance, we consolidated models that were developed using multiple random subsamples by majority voting. Reducing the number of features did not result in a significant decrease in the consolidated prediction accuracy.

BackgroundPain is one of the most prevalent health-related concerns and is among the top 3 most common reasons for seeking medical help. Scientific publications of data collected from pain tracking and monitoring apps are important to help consumers and healthcare professionals select the right app for their use. ObjectiveThe main objectives of this paper were to (1) discover user engagement patterns of the pain management app, Manage My Pain, using data mining methods; and (2) identify the association between several attributes characterizing individual users and their levels of engagement. MethodsUser engagement was defined by 2 key features of the app: longevity (number of days between the first and last pain record) and number of records. Users were divided into 5 user engagement clusters employing the k-means clustering algorithm. Each cluster was characterized by 6 attributes: gender, age, number of pain conditions, number of medications, pain severity, and opioid use. Z tests and chi-square tests were used for analyzing categorical attributes. Effects of gender and cluster on numerical attributes were analyzed using 2-way analysis of variances (ANOVAs) followed up by pairwise comparisons using Tukey honest significant difference (HSD). ResultsThe clustering process produced 5 clusters representing different levels of user engagement. The proportion of males and females was significantly different in 4 of the 5 clusters (all P ≤.03). The proportion of males was higher than females in users with relatively high longevity. Mean ages of users in 2 clusters with high longevity were higher than users from other 3 clusters (all P

Cannabidiol (CBD), a bicyclic non-psychoactive cannabinoid biosynthesized by Cannabis spp. of plants, has attracted significant interest in the past decade due to its therapeutic properties. In 2018, the US FDA approved Epidiolex®, a CBD-based drug for the treatment of two rare epileptic seizure disorders. CBD possesses two chiral centers at C3 and C4 on its terpenoid moiety and exhibits cis-trans stereoisomerism along the C3-C4 bond axis. (-)-trans-(3R,4R)-CBD, the natural CBD, is biosynthesized by the cannabis plant, while the unnatural (+)-trans-(3S,4S)-CBD is obtained via chemical synthesis. Both trans isomers exhibit broad in vitro and in vivo biological activities; typically, the unnatural stereoisomer (+)-trans-CBD and its derivatives exhibited more potent activities in comparison to the corresponding (-)-trans isomers. On the other hand, cis-CBD isomers have only been reported recently and can undergo epimerization into trans isomers. There is a significant opportunity to explore unique synthetic methods and biological activities of stereoisomers of CBD that may pave the path for the development of novel therapeutics. Herein, as a novel direction in cannabinoids, we review the chemistry of CBD stereoisomers, their structure-activity relationships, target selectivity and efficacy in animal models., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Lakshmi P. Kotra reports financial support was provided by Canadian Institutes of Health Research. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Excessive fructose intake is a risk factor for the development of obesity and its complications. Targeting ketohexokinase (KHK), the first enzyme of fructose metabolism, has been investigated for the management of metabolic dysfunction-associated steatotic liver disease (MASLD). We compared the effects of systemic, small molecule inhibitor of KHK enzymatic activity with hepatocyte-specific, N-acetylgalactosamine siRNA-mediated knockdown of KHK in mice on an HFD. We measured KHK enzymatic activity, extensively quantified glycogen accumulation, performed RNA-Seq analysis, and enumerated hepatic metabolites using mass spectrometry. Both KHK siRNA and KHK inhibitor led to an improvement in liver steatosis; however, via substantially different mechanisms, KHK knockdown decreased the de novo lipogenesis pathway, whereas the inhibitor increased the fatty acid oxidation pathway. Moreover, KHK knockdown completely prevented hepatic fructolysis and improved glucose tolerance. Conversely, the KHK inhibitor only partially reduced fructolysis, but it also targeted triokinase, mediating the third step of fructolysis. This led to the accumulation of fructose-1 phosphate, resulting in glycogen accumulation, hepatomegaly, and impaired glucose tolerance. Overexpression of wild-type, but not kinase-dead, KHK in cultured hepatocytes increased hepatocyte injury and glycogen accumulation after treatment with fructose. The differences between KHK inhibition and knockdown are, in part, explained by the kinase-dependent and -independent effects of KHK on hepatic metabolism.

Substance use disorders, including alcohol use disorder, are a public health concern that affect more than 150 million people globally. The opioid antagonist naltrexone is being increasingly prescribed to treat opioid use disorder, alcohol use disorder, and chronic pain. Perioperative management of patients on naltrexone is inconsistent and remains a controversial topic, with mismanagement posing a significant risk to the long-term health of these patients. This scoping review was conducted to identify human studies in which the perioperative management of naltrexone was described. This review includes a systematic literature search involving Medline, Medline In-Process, Embase, PsycINFO, and Web of Science. Seventeen articles that describe perioperative naltrexone management strategies were included, including thirteen guidelines, one case report, and three randomized trials. Despite its use in patients with alcohol use disorder and chronic pain, no clinical studies, case reports, or guidelines addressed naltrexone use in these clinical populations. All of the guideline documents recommended the preoperative cessation of naltrexone, irrespective of dose, indication, or route of administration. None of these guideline documents were designed on the basis of a systematic literature search or a Delphi protocol. As described by the primary studies, perioperative pain relief varied depending on naltrexone dose and route of administration, time since last naltrexone administration, and underlying substance use disorder. None of the studies commented on the maintenance of recovery for the patient's substance use disorder in the context of perioperative naltrexone management. The current understanding of the risks and benefits of continuing or stopping naltrexone perioperatively is limited by a lack of high-quality evidence. In patients with risk factors for return to use of opioids or alcohol, the discontinuation of naltrexone should have a strong rationale. Future studies and guidelines should seek to address both acute pain management and maintaining recovery when discussing perioperative naltrexone management strategies., (Copyright © 2024 American Society of Anesthesiologists. All Rights Reserved.)

Dopaminergic signaling in the nucleus accumbens shell (NAc) regulates neuronal activity relevant to reward-related learning, including cocaine-associated behaviors. Although astrocytes respond to dopamine and cocaine with structural changes, the impact of dopamine and cocaine on astrocyte functional plasticity has not been widely studied. Specifically, behavioral implications of voltage-gated channel activity in the canonically non-excitable astrocytes are not known. We characterized potassium channel function in NAc astrocytes following exposure to exogenous dopamine or cocaine self-administration training under short (2 h/day) and extended (6 h/day) access schedules. Electrophysiological, Ca 2+ imaging, mRNA, and mass spectrometry tools were used for molecular characterization. Behavioral effects were examined after NAc-targeted microinjections of channel antagonists and astroglial toxins. Exogenous dopamine increased activity of currents mediated by voltage-gated (K v 7) channels in NAc astrocytes. This was associated with a ~5-fold increase in expression of Kcnq2 transcript level in homogenized NAc micropunches. Matrix-assisted laser desorption/ionization mass spectrometry revealed increased NAc dopamine levels in extended access, relative to short access, rats. K v 7 inhibition selectively increased frequency and amplitude of astrocyte intracellular Ca 2+ transients in NAc of extended access rats. Inhibition of K v 7 channels in the NAc attenuated cocaine-seeking in extended access rats only, an effect that was occluded by microinjection of the astrocyte metabolic poison, fluorocitrate. These results suggest that voltage-gated K + channel signaling in NAc astrocytes is behaviorally relevant, support K v 7-mediated regulation of astrocyte Ca 2+ signals, and propose novel mechanisms of neuroglial interactions relevant to drug use., (© 2023. The Author(s), under exclusive licence to American College of Neuropsychopharmacology.)

Spinal cord injury (SCI) is a devastating condition characterized by impaired motor and sensory function, as well as internal organ pathology and dysfunction. This internal organ dysfunction, particularly gastrointestinal (GI) complications, and neurogenic bowel, can reduce the quality of life of individuals with an SCI and potentially hinder their recovery. The gut microbiome impacts various central nervous system functions and has been linked to a number of health and disease states. An imbalance of the gut microbiome, i.e., gut dysbiosis, contributes to neurological disease and may influence recovery and repair processes after SCI. Here we examine the impact of high cervical SCI on the gut microbiome and find that transient gut dysbiosis with persistent gut pathology develops after SCI. Importantly, probiotic treatment improves gut health and respiratory motor function measured through whole-body plethysmography. Concurrent with these improvements was a systemic decrease in the cytokine tumor necrosis factor-alpha and an increase in neurite sprouting and regenerative potential of neurons. Collectively, these data reveal the gut microbiome as an important therapeutic target to improve visceral organ health and respiratory motor recovery after SCI., Research Highlights: Cervical spinal cord injury (SCI) causes transient gut dysbiosis and persistent gastrointestinal (GI) pathology.Treatment with probiotics after SCI leads to a healthier GI tract and improved respiratory motor recovery.Probiotic treatment decreases systemic tumor necrosis factor-alpha and increases the potential for sprouting and regeneration of neurons after SCI.The gut microbiome is a valid target to improve motor function and secondary visceral health after SCI.

Background and Aims: Genome and epigenome wide association studies identified variants in carnitine palmitoyltransferase 1a (CPT1a) that associate with lipid traits. The goal of this study was to determine the role of liver-specific CPT1a on hepatic lipid metabolism., Approach and Results: Male and female liver-specific knockout (LKO) and littermate controls were placed on a low-fat or high-fat diet (60% kcal fat) for 15 weeks. Mice were necropsied after a 16 h fast, and tissues were collected for lipidomics, matrix-assisted laser desorption ionization mass spectrometry imaging, kinome analysis, RNA-sequencing, and protein expression by immunoblotting. Female LKO mice had increased serum alanine aminotransferase levels which were associated with greater deposition of hepatic lipids, while male mice were not affected by CPT1a deletion relative to male control mice. Mice with CPT1a deletion had reductions in DHA-containing phospholipids at the expense of monounsaturated fatty acids (MUFA)-containing phospholipids in whole liver and at the level of the lipid droplet (LD). Male and female LKO mice increased RNA levels of genes involved in LD lipolysis (Plin2, Cidec, G0S2) and in polyunsaturated fatty acid metabolism (Elovl5, Fads1, Elovl2), while only female LKO mice increased genes involved in inflammation (Ly6d, Mmp12, Cxcl2). Kinase profiling showed decreased protein kinase A activity, which coincided with increased PLIN2, PLIN5, and G0S2 protein levels and decreased triglyceride hydrolysis in LKO mice., Conclusions: Liver-specific deletion of CPT1a promotes sexually dimorphic steatotic liver disease (SLD) in mice, and here we have identified new mechanisms by which females are protected from HFD-induced liver injury., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier GmbH.. All rights reserved.)