Opioid Tolerance and Physical Dependence: Role of Spinal Neuropeptides, Excitatory Amino Acids and Their Messengers
Bibliographic record
Abstract
Chronic opioid treatment results in the development of tolerance and physical dependence. The mechanisms underlying opioid tolerance and/or physical dependence are unclear. Recent studies suggest that opioid receptor or nociceptive, neural network‐based adaptations contribute to this phenomenon. At the spinal level, the genesis of tolerance and physical dependence is associated with increased excitatory amino acid activity expressed through N‐methyl‐D‐aspartate receptors in the dorsal horn. However, recent evidence also implicates spinal neuropeptide transmitters such as calcitonin gene‐related peptide (CGRP) and substance P in the development of opioid tolerance. Long term spinal morphine treatment increases CGRP‐like immunostaining in the dorsal horn, and coadministration of morphine with CGRP8-37, a competitive CGRP1 receptor antagonist, prevents this response as well as loss of the analgesic potency. CGRP8-37, like N‐methyl‐D‐aspartate receptor antagonists, has the potential to restore morphine potency in experimental animals who are already tolerant to the opioid agonist. Recent evidence suggests that the effects of excitatory amino acid and neuropeptide receptor activity may be expressed through the generation of messengers such as nitric oxide and prostanoids. Agents that inhibit the synthesis of nitric oxide and prostanoids have the potential to inhibit and reverse spinal opioid tolerance, suggesting that this phenomenon may be expressed through the activity of these mediators. Nociceptive transmission in the dorsal horn of the spinal cord also involves activity of a number of other mediators including morphine modulatory neuropeptides, neuropeptide FF and neuropeptide SF. The role of these mediators and their relationship with other factors implicated in tolerance remain to be determined.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".