Altered Hormone and Bioactive Lipid Plasma Profile in Rodent Models of Polycystic Ovarian Syndrome Revealed by Targeted Mass Spectrometry
Bibliographic record
Abstract
Abstract Polycystic ovarian syndrome (PCOS) symptoms include excessive body or facial hair, irregular periods, reduced fertility, and reoccurring pregnancy loss. Hyperandrogenism and chronic inflammation are hallmarks of PCOS, which is diagnosed by analysing steroid hormones in the blood. Studies suggest that bioactive lipids are contributing to chronic inflammation. To research PCOS, animal models, such as letrozole- and dihydrotestosterone-treated rats, are used. They display similar ovarian and metabolic characteristics, although plasma lipid profiles have not been determined. Therefore, in order to validate the use of these models for PCOS, we have optimised a mass spectrometry based targeted lipidomics workflow which increases the sensitivity of measuring these lipids in rat plasma. Our analysis shows that letrozole caused a significant elevation of 5α-androstene-3,17-dione and testosterone. Dihydrotestosterone treatment resulted in increased dehydroepiandrosterone-sulphate and allopregnanolone but a reduction in testosterone, progesterone, pregnenolone, and D-sphingosine. In both models 25-hydroxycholesterol and leukotriene C 4 were significantly diminished and 4-cholesten-3-one was significantly increased, and these particular metabolites are not known to be changed in human PCOS. These results suggest that the plasma lipids of these rodent models have some changes akin to human PCOS and other changes that are not.
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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) | 0.000 | 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) | 0.001 | 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".