Neurobiology of opioid dependence in creating addiction vulnerability
Bibliographic record
Abstract
Opioid drugs are potent modulators of many physiological and psychological processes. When given acutely, they can elicit the signature responses of euphoria and analgesia that societies have coveted for centuries. Repeated, or chronic, use of opioids induces adaptive or allostatic changes that modify neuronal circuitry and create an altered normality - the "drug-dependent" state. This state, at least that exhibited by those maintained continuously on long-acting opioid drugs such as methadone or buprenorphine, is generally indistinguishable from the drug-naïve state for most overt behaviors. The consequences of the allostatic changes (cellular, circuit, and system adaptations) that accompany the drug-dependent state are revealed during drug withdrawal. Drug cessation triggers a temporally orchestrated allostatic re-establishment of neuronal systems, which is manifested as opposing physiological and psychological effects to those exhibited by acute drug intoxication. Some withdrawal symptoms, such as physical symptoms (sweating, shaking, and diarrhea) resolve within days, whilst others, such as dysphoria, insomnia, and anxiety, can linger for months, and some adaptations, such as learned associations, may be established for life. We will briefly discuss the cellular mechanisms and neural circuitry that contribute to the opioid drug-dependent state, inferring an emerging role for neuroinflammation. We will argue that opioid addictive behaviors result from a learned relationship between opioids and relief from an existing or withdrawal-induced anxiogenic and/or dysphoric state. Furthermore, a future stressful life event can recall the memory that opioid drugs alleviate negative affect (despair, sadness, and anxiety) and thereby precipitate craving, resulting in relapse. A learned association of relief of aversive states would fuel drug craving in vulnerable people living in an increasingly stressful society. We suggest that this route to addiction is contributive to the current opioid epidemic in the USA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".