Dissociation between morphine-induced spinal gliosis and analgesic tolerance by ultra-low-dose α2-adrenergic and cannabinoid CB1-receptor antagonists
Bibliographic record
Abstract
Long-term use of opioid analgesics is limited by tolerance development and undesirable adverse effects. Paradoxically, spinal administration of ultra-low-dose (ULD) G-protein-coupled receptor antagonists attenuates analgesic tolerance. Here, we determined whether systemic ULD α2-adrenergic receptor (AR) antagonists attenuate the development of morphine tolerance, whether these effects extend to the cannabinoid (CB1) receptor system, and if behavioral effects are reflected in changes in opioid-induced spinal gliosis. Male rats were treated daily with morphine (5 mg/kg) alone or in combination with ULD α2-AR (atipamezole or efaroxan; 17 ng/kg) or CB1 (rimonabant; 5 ng/kg) antagonists; control groups received ULD injections only. Thermal tail flick latencies were assessed across 7 days, before and 30 min after the injection. On day 8, spinal cords were isolated, and changes in spinal gliosis were assessed through fluorescent immunohistochemistry. Both ULD α2-AR antagonists attenuated morphine tolerance, whereas the ULD CB1 antagonist did not. In contrast, both ULD atipamezole and ULD rimonabant attenuated morphine-induced microglial reactivity and astrogliosis in deep and superficial spinal dorsal horn. So, although paradoxical effects of ULD antagonists are common to several G-protein-coupled receptor systems, these may not involve similar mechanisms. Spinal glia alone may not be the main mechanism through which tolerance is modulated.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".