The Effects of Exercise on Pain and Reproductive Performance in Female Pregnant Mice With Neuropathic Pain
Bibliographic record
Abstract
Pain can have negative, physiological and psychological impacts on pregnancy. Pregnant women are fearful of using pain medication because of teratogenic effects. In this study, we evaluated whether exercise could lower pain sensitivity in pregnant mice with neuropathic pain and reduce the negative effects of maternal pain on newborns. We randomly assigned 32 female mice to one of four groups (eight mice/group): Sham surgery with standard environment (SE) or enriched environment (EE) or spare nerve injury (SNI) with SE or EE. Mice in EE groups had access to an exercise wheel. Mothers were evaluated for mechanical sensitivity with Von Frey filaments and for exercise performance with computerized running wheels. Mice were impregnated 2 weeks after the initiation of EE. Pups were weighed and measured for length at birth and evaluated for negative geotaxis, righting, forelimb grasping, rooting, and crawling at 3 days postpartum and for crawling at 6 days postpartum. Following euthanasia, mothers' frontal cortexes were analyzed for selected neuropeptides. After exercise exposure, only SNI-SE females remained neuropathic. Exercise levels were similar between EE groups. Some brain neuropeptides (endorphins, enkephalins, and oxytocin) from SNI females showed significant differences with exercise. Number of pups was significantly smaller in the SNI-SE group. Significantly more pups died at birth in the SNI-SE group, but pup behavior tests (except righting) were similar across groups. Exercise can reduce neuropathic pain in pregnant mice. Neuropathic pain does not impact motor neurodevelopment of mice pups but does appear to affect litter size and neonatal mortality.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".