Ovarian Aging-Like Phenotype in the Hyperandrogenism-Induced Murine Model of Polycystic Ovary
Bibliographic record
Abstract
There are prominently similar symptoms, effectors, and commonalities in the majority of characteristics between ovarian aging and polycystic ovarian syndrome (PCOS). Despite the approved role of oxidative stress in the pathogenesis of PCOS and aging, to our knowledge, the link between the PCO(S) and aging has not been investigated yet. In this study we investigated the possible exhibition of ovarian aging phenotype in murine model of PCO induced by daily oral administration of letrozole (1 mg/kg body weight) for 21 consecutive days in the female Wistar rats. Hyperandrogenization showed irregular cycles and histopathological characteristics of PCO which was associated with a significant increase in lipid peroxidation (LPO) and reactive oxygen species (ROS) and decrease in total antioxidant capacity (TAC) in serum and ovary. Moreover, serum testosterone, insulin and tumor necrosis factor-alpha (TNF-α) levels, and ovarian matrix metalloproteinase-2 (MMP-2) were increased in PCO rats compared with healthy controls, while estradiol and progesterone diminished. Almost all of these findings are interestingly found to be common with the characteristics identified with (ovarian) aging showing that hyperandrogenism-induced PCO in rat is associated with ovarian aging-like phenotypes. To our knowledge, this is the first report that provides evidence regarding the phenomenon of aging in PCO.
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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 | 0.001 | 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".