T-Cell Mediation of Pregnancy Analgesia Affecting Chronic Pain in Mice
Bibliographic record
Abstract
It has been reported consistently that many female chronic pain sufferers have an attenuation of symptoms during pregnancy. Rats display increased pain tolerance during pregnancy due to an increase in opioid receptors in the spinal cord. Past studies did not consider the role of non-neuronal cells, which are now known to play an important role in chronic pain processing. Using an inflammatory (complete Freund's adjuvant) or neuropathic (spared nerve injury) model of persistent pain, we observed that young adult female mice in early pregnancy switch from a microglia-independent to a microglia-dependent pain hypersensitivity mechanism. During late pregnancy, female mice show no evidence of chronic pain whatsoever. This pregnancy-related analgesia is reversible by intrathecal administration of naloxone, suggesting an opioid-mediated mechanism; pharmacological and genetic data suggest the importance of δ-opioid receptors. We also observe that T-cell-deficient (nudeandRag1-null mutant) pregnant mice do not exhibit pregnancy analgesia, which can be rescued with the adoptive transfer of CD4+or CD8+T cells from late-pregnant wild-type mice. These results suggest that T cells are a mediator of the opioid analgesia exhibited during pregnancy. SIGNIFICANCE STATEMENTChronic pain symptoms often subside during pregnancy. This pregnancy-related analgesia has been demonstrated for acute pain in rats. Here, we show that pregnancy analgesia can produce a complete cessation of chronic pain behaviors in mice. We show that the phenomenon is dependent on pregnancy hormones (estrogen and progesterone), δ-opioid receptors, and T cells of the adaptive immune system. These findings add to the recent but growing evidence of sex-specific T-cell involvement in chronic pain processing.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".