Oxycodone-induced dopaminergic and respiratory effects are modulated by deep brain stimulation

Introduction: Opioids are the leading cause of overdose death in the United States, accounting for almost 70,000 deaths in 2020. Deep brain stimulation (DBS) is a promising new treatment for substance use disorders. Here, we hypothesized that VTA DBS would modulate both the dopaminergic and respiratory effect of oxycodone.Methods: Multiple-cyclic square wave voltammetry (M-CSWV) was used to investigate how deep brain stimulation (130 Hz, 0.2 ms, and 0.2 mA) of the rodent ventral segmental area (VTA), which contains abundant dopaminergic neurons, modulates the acute effects of oxycodone administration (2.5 mg/kg, i.v.) on nucleus accumbens core (NAcc) tonic extracellular dopamine levels and respiratory rate in urethane-anesthetized rats (1.5 g/kg, i.p.).Results: I.V. administration of oxycodone resulted in an increase in NAcc tonic dopamine levels (296.9 ± 37.0 nM) compared to baseline (150.7 ± 15.5 nM) and saline administration (152.0 ± 16.1 nM) (296.9 ± 37.0 vs. 150.7 ± 15.5 vs. 152.0 ± 16.1, respectively, p = 0.022, n = 5). This robust oxycodone-induced increase in NAcc dopamine concentration was associated with a sharp reduction in respiratory rate (111.7 ± 2.6 min−1 vs. 67.9 ± 8.3 min−1; pre- vs. post-oxycodone; p < 0.001). Continuous DBS targeted at the VTA (n = 5) reduced baseline dopamine levels, attenuated the oxycodone-induced increase in dopamine levels to (+39.0% vs. +95%), and respiratory depression (121.5 ± 6.7 min−1 vs. 105.2 ± 4.1 min−1; pre- vs. post-oxycodone; p = 0.072).Discussion: Here we demonstrated VTA DBS alleviates oxycodone-induced increases in NAcc dopamine levels and reverses respiratory suppression. These results support the possibility of using neuromodulation technology for treatment of drug addiction

The effects of parental opioid use on the parent–child relationship and children’s developmental and behavioral outcomes: a systematic review of published reports

Abstract Background Between 2009 and 2014, nearly 3% of US children (age ≤ 17 years) lived in households with at least 1 parent with substance use disorder. The present systematic review aimed to evaluate effects of parental opioid use disorder on the parent–child relationship and child developmental and behavioral outcomes. Methods Several databases were comprehensively searched for studies published from January 1980 through February 2018 that reviewed effects of parental opioid addiction on parent–child relationships and outcomes of children (age, 0–16 years). Results Of 304 unique studies, 12 evaluated effects of parental opioid addiction on the parent–child relationship as the primary outcome and on children’s outcomes, including behaviors and development. Observation of mother–child interaction showed that mothers with opioid use disorders are more irritable, ambivalent, and disinterested while showing greater difficulty interpreting children’s cues compared with the control group. Children of parents with opioid use disorders showed greater disorganized attachment; they were less likely to seek contact and more avoidant than children in the control group. The children also had increased risk of emotional and behavioral issues, poor academic performance, and poor social skills. Younger children had increased risk of abuse or neglect, or both, that later in life may lead to such difficulties as unemployment, legal issues, and substance abuse. Conclusions Current evidence shows association between parental opioid addiction and poorer mother–child attachment and suboptimal child developmental and behavioral outcomes. Further research and treatment targeting children and families with parental opioid use are needed to prevent difficulties later in life

A risk identification model for detection of patients at risk of antidepressant discontinuation

Funding Information: This research was partially supported by the Intramural Research Program of the National Institutes of Health (NIH), National Library of Medicine (NLM) and Lister Hill National Center for Biomedical Communications (LHNCBC). Publisher Copyright: Copyright © 2023 Zolnour, Eldredge, Faiola, Yaghoobzadeh, Khani, Foy, Topaz, Kharrazi, Fung, Fontelo, Davoudi, Tabaie, Breitinger, Oesterle, Rouhizadeh, Zonnor, Moen, Patrick and Zolnoori.Purpose: Between 30 and 68% of patients prematurely discontinue their antidepressant treatment, posing significant risks to patient safety and healthcare outcomes. Online healthcare forums have the potential to offer a rich and unique source of data, revealing dimensions of antidepressant discontinuation that may not be captured by conventional data sources. Methods: We analyzed 891 patient narratives from the online healthcare forum, “askapatient.com,” utilizing content analysis to create PsyRisk—a corpus highlighting the risk factors associated with antidepressant discontinuation. Leveraging PsyRisk, alongside PsyTAR [a publicly available corpus of adverse drug reactions (ADRs) related to antidepressants], we developed a machine learning-driven algorithm for proactive identification of patients at risk of abrupt antidepressant discontinuation. Results: From the analyzed 891 patients, 232 reported antidepressant discontinuation. Among these patients, 92% experienced ADRs, and 72% found these reactions distressful, negatively affecting their daily activities. Approximately 26% of patients perceived the antidepressants as ineffective. Most reported ADRs were physiological (61%, 411/673), followed by cognitive (30%, 197/673), and psychological (28%, 188/673) ADRs. In our study, we employed a nested cross-validation strategy with an outer 5-fold cross-validation for model selection, and an inner 5-fold cross-validation for hyperparameter tuning. The performance of our risk identification algorithm, as assessed through this robust validation technique, yielded an AUC-ROC of 90.77 and an F1-score of 83.33. The most significant contributors to abrupt discontinuation were high perceived distress from ADRs and perceived ineffectiveness of the antidepressants. Conclusion: The risk factors identified and the risk identification algorithm developed in this study have substantial potential for clinical application. They could assist healthcare professionals in identifying and managing patients with depression who are at riskof prematurely discontinuing their antidepressant treatment.Peer reviewe