Omega-3 polyunsaturated fatty acid docosahexaenoic acid and its role in exhaustive-exercise-induced changes in female rat ovulatory cycle
Bibliographic record
Abstract
Exhaustive exercises can cause delayed menarche or menstrual cycle irregularities in females. Omega-3 polyunsaturated fatty acids (ω-3 PUFAs) are incorporated into a wide range of benefits in many physiological systems. Our work aimed to assess the role of ω-3 PUFA docosahexaenoic acid (DHA) on the deleterious effects of exhaustive exercise on the female reproductive system in rats. Virgin female rats were randomly divided into 4 groups (12 rats in each): control group, omega-3 group treated with DHA, exhaustive exercise group, and exhaustive exercised rats treated with DHA. Omega-3 was given orally to the rats once daily for 4 estrous cycles. Exhaustive exercises revealed lower levels in progesterone and gonadotropins together with histopathological decrease in number of growing follicles and corpora lutea. Moreover, the exercised rats showed low levels of ovarian antioxidants with high level of caspase-3 and plasma cortisol level that lead to disruption of hypothalamic-pituitary-gonadal axis. ω-3 PUFA DHA has beneficial effects on the number of newly growing follicles in both sedentary and exercised rats with decreasing the level of caspase-3 and increasing the antioxidant activity in ovaries. Exhaustive exercises can cause ovulatory problems in female rats that can be improved by ω-3 supplementation.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".