Sex and Strain Differences in Analgesic and Hyperlocomotor Effects of Morphine and μ‐Opioid Receptor Expression in Mice
Bibliographic record
Abstract
Sex and gender differences in the analgesic efficacy and side effects of opioids have been widely reported, but their underlying neurobiological mechanisms remain poorly understood. Preclinical animal models are essential tools for investigating these differences and providing insights into the neurobiology of opioid effects. Although studies in rats have revealed sex-specific effects of opioids, the sex-dependent behavioral profiles of opioids in mice, particularly across strains, remain largely unexplored. In this study, we characterized sex and strain differences in the antinociceptive and hyperlocomotor effects of morphine in the two most widely used mouse strains-CD1 and C57BL/6-and quantified regional expression of the μ-opioid receptor (MOR) in key brain and spinal cord regions. Both strains exhibited clear, dose-dependent antinociceptive and hyperlocomotor responses to morphine. While no significant sex or strain differences were observed in antinociceptive effects, C57BL/6 mice displayed significantly greater hyperlocomotor activity than CD1 mice. Western blot analyses revealed strain-specific MOR expression, with CD1 mice showing higher spinal cord and periaqueductal gray MOR levels, particularly in females, while C57BL/6 mice exhibited elevated MOR expression in the caudoputamen. Morphine treatment increased spinal MOR expression in CD1 mice but not C57BL/6, suggesting strain-dependent regulation of MOR. These findings highlight strain-specific behavioral and molecular responses to morphine, emphasizing the importance of strain and sex considerations in preclinical opioid research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".