Morphine Withdrawal-Induced Hyperalgesia in Models of Acute and Extended Withdrawal Is Attenuated by l-Tetrahydropalmatine
Bibliographic record
Abstract
Effective pain control is an underappreciated aspect of managing opioid withdrawal, and its absence presents a significant barrier to successful opioid detoxification. Accordingly, there is an urgent need for effective non-opioid treatments to facilitate opioid detoxification. l-Tetrahydropalmatine (l-THP) possesses powerful analgesic properties and is an active ingredient in botanical formulations used in Vietnam for the treatment of opioid withdrawal syndrome. In this study, rats receiving morphine (15 mg/kg, i.p.) for 5 days per week displayed a progressive increase in pain thresholds during acute 23 h withdrawal as assessed by an automated Von Frey test. A single dose of l-THP (5 or 7.5 mg/kg, p.o.) administered during the 4th and 5th weeks of morphine treatment significantly improves pain tolerance scores. A 7-day course of l-THP treatment in animals experiencing extended withdrawal significantly attenuates hyperalgesia and reduces the number of days to recovery to baseline pain thresholds by 61% when compared to vehicle-treated controls. This indicates that the efficacy of l-THP on pain perception extends beyond its half-life. As a non-opioid treatment for reversing a significant hyperalgesic state during withdrawal, l-THP may be a valuable addition to the currently limited arsenal of opioid detoxification treatments.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".