Endothelin ET<sub>A</sub> receptor antagonist reverses naloxone-precipitated opioid withdrawal in mice
Bibliographic record
Abstract
Long-term use of opioids for pain management results in rapid development of tolerance and dependence leading to severe withdrawal symptoms. We have previously demonstrated that endothelin-A (ETA) receptor antagonists potentiate opioid analgesia and eliminate analgesic tolerance. This study was designed to investigate the involvement of central ET mechanisms in opioid withdrawal. The effect of intracerebroventricular administration of ETA receptor antagonist BQ123 on morphine and oxycodone withdrawal was determined in male Swiss Webster mice. Opioid tolerance was induced and withdrawal was precipitated by the opioid antagonist naloxone. Expression of ETA and ETB receptors, nerve growth factor (NGF), and vascular endothelial growth factor was determined in the brain using Western blotting. BQ123 pretreatment reversed hypothermia and weight loss during withdrawal. BQ123 also reduced wet shakes, rearing behavior, and jumping behavior. No changes in expression of vascular endothelial growth factor, ETA receptors, and ETB receptors were observed during withdrawal. NGF expression was unaffected in morphine withdrawal but significantly decreased during oxycodone withdrawal. A decrease in NGF expression in oxycodone- but not in morphine-treated mice could be due to mechanistic differences in oxycodone and morphine. It is concluded that ETA receptor antagonists attenuate opioid-induced withdrawal symptoms.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".