Sex differences in kappa opioid receptor antinociception is influenced by the number of X chromosomes in mouse
Bibliographic record
Abstract
Kappa opioid receptor (KOR) agonists produce robust analgesia with minimal abuse liability and are considered promising pharmacological agents to manage chronic pain and itch. The KOR system is also notable for robust differences between the sexes, with females exhibiting lower analgesic response than males. Sexually dimorphic traits can be due to either the influence of gonadal hormones during development or adulthood, or due to the complement of genes expressed on the X or Y chromosome. Previous studies examining sex differences in KOR antinociception have relied on surgical or pharmacological manipulation of the gonads to determine whether sex hormones influence KOR function. While there are conflicting reports whether gonadal hormones influence KOR function, no study has examined these effects in context with sex chromosomes. Here, we use two genetic mouse models, the four core genotypes and XY*, to isolate the chromosomal and hormonal contributions to sex differences in KOR analgesia. Mice were treated with systemic KOR agonist (U50,488H) and thermal analgesia measured in the tail withdrawal assay. We found that KOR antinociception was influenced predominantly by the number of the X chromosomes. These data suggest that the dose and/or parental imprint on X gene(s) contribute significantly to the sexually dimorphism in KOR analgesia.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".