Piloting Smart Home Sensors to Detect Overnight Respiratory and Withdrawal Symptoms in Adults Prescribed Opioids

The cell-permeant antioxidant D-thiol ester D-cysteine ethyl ester overcomes physical dependence to morphine in male Sprague Dawley rats

The ability of morphine to decrease cysteine transport into neurons by inhibition of excitatory amino acid transporter 3 (EAA3) may be a key molecular mechanism underlying the acquisition of physical and psychological dependence to morphine. This study examined whether co-administration of the cell-penetrant antioxidant D-thiol ester, D-cysteine ethyl ester (D-CYSee), with morphine, would diminish the development of physical dependence to morphine in male Sprague Dawley rats. Systemic administration of the opioid receptor antagonist, naloxone (NLX), elicited pronounced withdrawal signs (e.g., wet-dog shakes, jumps, rears, circling) in rats that received a subcutaneous depot of morphine (150 mg/kg, SC) for 36 h and continuous intravenous infusion of vehicle (20 μL/h, IV). The NLX-precipitated withdrawal signs were reduced in rats that received an infusion of D-CYSee, but not D-cysteine, (both at 20.8 μmol/kg/h, IV) for the full 36 h. NLX elicited pronounced withdrawal signs in rats treated for 48 h with morphine (150 mg/kg, SC), plus continuous infusion of vehicle (20 μL/h, IV) that began at the 36 h timepoint of morphine treatment. The NLX-precipitated withdrawal signs were reduced in rats that received a 12 h infusion of D-CYSee, but not D-cysteine, (both at 20.8 μmol/kg/h, IV) that began at the 36 h timepoint of morphine treatment. These findings suggest that D-CYSee may attenuate the development of physical dependence to morphine and reverse established dependence to the opioid in male Sprague Dawley rats. Alternatively, D-CYSee may simply suppress the processes responsible for NLX-precipitated withdrawal. Nonetheless, D-CYSee and analogues may be novel therapeutics for the treatment of opioid use disorders.

Perspectives on Online Resources for People Experiencing Pain: A Qualitative Study

BACKGROUND

Innovations in technology offer potential solutions to address pain care inequities. To maximize impacts, greater understanding is needed regarding preferences and priorities of people experiencing or treating pain.

OBJECTIVES

This study conducted focus groups to investigate the perspectives of people with pain and healthcare workers regarding online resources for pain management. Researchers asked about barriers to current pain management and what resources would be most desired in an online format to meet needs.

METHODS

Participants were a randomly selected sub-sample of adults from a northwestern region of the United States who participated in an online, survey-based study. Eligible participants identified as either a person who had received treatment for pain or a healthcare worker who cared for people with pain. Of the 199 survey respondents, 30 participated in one of three focus group sessions. Focus groups were conducted using videoconferencing technology, then recorded, transcribed, and analyzed using thematic analysis.

RESULTS

Focus group participants included 22 adults who identified as a person treated for pain of any type and 8 healthcare workers. Themes relating to eHealth use reflected desires for (1) freely accessible and vetted pain management information in one place, (2) reliable information tailored to need and pain type, and (3) easy-to-use resources. Findings revealed that some effective pain management resources do exist, yet obstacles including inflexible and inequitable healthcare practices and lack of knowledge about options may limit access to these resources.

CONCLUSION

Including preferences of user groups can assist in creating resources that are likely to be useful for those with pain and their caregivers. Innovations are needed to address persisting gaps in care.

Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review

Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.

Lipophilic analogues of D-cysteine prevent and reverse physical dependence to fentanyl in male rats

We examined whether co-injections of the cell-permeant D-cysteine analogues, D-cysteine ethyl ester (D-CYSee) and D-cysteine ethyl amide (D-CYSea), prevent acquisition of physical dependence induced by twice-daily injections of fentanyl, and reverse acquired dependence to these injections in freely-moving male Sprague Dawley rats. Injection of the opioid receptor antagonist, naloxone HCl (NLX, 1.5 mg/kg, IV), elicited a series of withdrawal phenomena that included cardiorespiratory and behavioral responses, and falls in body weight and body temperature, in rats that received 5 or 10 injections of fentanyl (125 μg/kg, IV), and the same number of vehicle co-injections. Regarding the development of physical dependence, the NLX-precipitated withdrawal phenomena were markedly reduced in fentanyl-injected rats that had received co-injections of D-CYSee (250 μmol/kg, IV) or D-CYSea (100 μmol/kg, IV), but not D-cysteine (250 μmol/kg, IV). Regarding reversal of established dependence to fentanyl, the NLX-precipitated withdrawal phenomena in rats that had received 10 injections of fentanyl (125 μg/kg, IV) was markedly reduced in rats that received co-injections of D-CYSee (250 μmol/kg, IV) or D-CYSea (100 μmol/kg, IV), but not D-cysteine (250 μmol/kg, IV), starting with injection 6 of fentanyl. This study provides evidence that co-injections of D-CYSee and D-CYSea prevent the acquisition of physical dependence, and reverse acquired dependence to fentanyl in male rats. The lack of effect of D-cysteine suggests that the enhanced cell-penetrability of D-CYSee and D-CYSea into cells, particularly within the brain, is key to their ability to interact with intracellular signaling events involved in acquisition to physical dependence to fentanyl.

Enhancing Human Activity Recognition in Smart Homes with Self-Supervised Learning and Self-Attention

Deep learning models have gained prominence in human activity recognition using ambient sensors, particularly for telemonitoring older adults' daily activities in real-world scenarios. However, collecting large volumes of annotated sensor data presents a formidable challenge, given the time-consuming and costly nature of traditional manual annotation methods, especially for extensive projects. In response to this challenge, we propose a novel AttCLHAR model rooted in the self-supervised learning framework SimCLR and augmented with a self-attention mechanism. This model is designed for human activity recognition utilizing ambient sensor data, tailored explicitly for scenarios with limited or no annotations. AttCLHAR encompasses unsupervised pre-training and fine-tuning phases, sharing a common encoder module with two convolutional layers and a long short-term memory (LSTM) layer. The output is further connected to a self-attention layer, allowing the model to selectively focus on different input sequence segments. The incorporation of sharpness-aware minimization (SAM) aims to enhance model generalization by penalizing loss sharpness. The pre-training phase focuses on learning representative features from abundant unlabeled data, capturing both spatial and temporal dependencies in the sensor data. It facilitates the extraction of informative features for subsequent fine-tuning tasks. We extensively evaluated the AttCLHAR model using three CASAS smart home datasets (Aruba-1, Aruba-2, and Milan). We compared its performance against the SimCLR framework, SimCLR with SAM, and SimCLR with the self-attention layer. The experimental results demonstrate the superior performance of our approach, especially in semi-supervised and transfer learning scenarios. It outperforms existing models, marking a significant advancement in using self-supervised learning to extract valuable insights from unlabeled ambient sensor data in real-world environments.

L-cysteine ethyl ester prevents and reverses acquired physical dependence on morphine in male Sprague Dawley rats

The molecular mechanisms underlying the acquisition of addiction/dependence on morphine may result from the ability of the opioid to diminish the transport of L-cysteine into neurons via inhibition of excitatory amino acid transporter 3 (EAA3). The objective of this study was to determine whether the co-administration of the cell-penetrant L-thiol ester, L-cysteine ethyl ester (L-CYSee), would reduce physical dependence on morphine in male Sprague Dawley rats. Injection of the opioid-receptor antagonist, naloxone HCl (NLX; 1.5 mg/kg, IP), elicited pronounced withdrawal phenomena in rats which received a subcutaneous depot of morphine (150 mg/kg) for 36 h and were receiving a continuous infusion of saline (20 μL/h, IV) via osmotic minipumps for the same 36 h period. The withdrawal phenomena included wet-dog shakes, jumping, rearing, fore-paw licking, 360° circling, writhing, apneas, cardiovascular (pressor and tachycardia) responses, hypothermia, and body weight loss. NLX elicited substantially reduced withdrawal syndrome in rats that received an infusion of L-CYSee (20.8 μmol/kg/h, IV) for 36 h. NLX precipitated a marked withdrawal syndrome in rats that had received subcutaneous depots of morphine (150 mg/kg) for 48 h) and a co-infusion of vehicle. However, the NLX-precipitated withdrawal signs were markedly reduced in morphine (150 mg/kg for 48 h)-treated rats that began receiving an infusion of L-CYSee (20.8 μmol/kg/h, IV) at 36 h. In similar studies to those described previously, neither L-cysteine nor L-serine ethyl ester (both at 20.8 μmol/kg/h, IV) mimicked the effects of L-CYSee. This study demonstrates that 1) L-CYSee attenuates the development of physical dependence on morphine in male rats and 2) prior administration of L-CYSee reverses morphine dependence, most likely by intracellular actions within the brain. The lack of the effect of L-serine ethyl ester (oxygen atom instead of sulfur atom) strongly implicates thiol biochemistry in the efficacy of L-CYSee. Accordingly, L-CYSee and analogs may be a novel class of therapeutics that ameliorate the development of physical dependence on opioids in humans.

Adaptive interventions for opioid prescription management and consumption monitoring

OBJECTIVES

While opioid addiction, treatment, and recovery are receiving attention, not much has been done on adaptive interventions to prevent opioid use disorder (OUD). To address this, we identify opioid prescription and opioid consumption as promising targets for adaptive interventions and present a design framework.

MATERIALS AND METHODS

Using the framework, we designed Smart Prescription Management (SPM) and Smart Consumption Monitoring (SCM) interventions. The interventions are evaluated using analytical modeling and secondary data on doctor shopping, opioid overdose, prescription quality, and cost components.

RESULTS

SPM was most effective (30-90% improvement, for example, prescriptions reduced from 18 to 1.8 per patient) for extensive doctor shopping and reduced overdose events and mortality. Opioid adherence was improved and the likelihood of addiction declined (10-30%) as the response rate to SCM was increased. There is the potential for significant incentives ($2267-$3237) to be offered for addressing severe OUD.

DISCUSSION

The framework and designed interventions adapt to changing needs and conditions of the patients to become an important part of global efforts in preventing OUD. To the best of our knowledge, this is the first paper on adaptive interventions for preventing OUD by addressing both prescription and consumption.

CONCLUSION

SPM and SCM improved opioid prescription and consumption while reducing the risk of opioid addiction. These interventions will assist in better prescription decisions and in managing opioid consumption leading to desirable outcomes. The interventions can be extended to other substance use disorders and to study complex scenarios of prescription and nonprescription opioids in clinical studies.