Induction of a menopausal state alters the growth and histology of ovarian tumors in a mouse model of ovarian cancer
Bibliographic record
Abstract
OBJECTIVE: Ovarian cancer is often diagnosed in women after menopause when the levels of the serum gonadotropins follicle-stimulating hormone (FSH) and luteinizing hormone (LH) are increased because of the depletion of growing follicles within the ovary. The ability of FSH and LH to modulate the disease has not been well studied owing to a lack of physiologically relevant models of ovarian cancer. In this study, 4-vinylcyclohexene diepoxide (VCD) was used to deplete ovarian follicles and increase the levels of circulating FSH and LH in the tgCAG-LS-TAg mouse model of ovarian cancer. METHODS: VCD-induced follicle depletion was performed either before or after induction of the oncogene SV40 large and small T-antigens in the ovarian surface epithelial cells of tgCAG-LS-TAg mice, which was mediated by the intrabursal delivery of an adenovirus expressing Cre recombinase (AdCre). RESULTS: tgCAG-LS-TAg mice injected with AdCre developed undifferentiated ovarian tumors with mixed epithelial and stromal components and some features of sex cord stromal tumors. Treatment with VCD before or after AdCre injection yielded tumors of similar histology, but with the unique appearance of Sertoli cell nests. In mice treated with VCD before the induction of tumorigenesis, the ovarian tumors tended to grow more slowly. The human ovarian cancer cell lines SKOV3 and OVCAR3 responded similarly to increased levels of gonadotropins in a second model of menopause, growing more slowly in ovariectomized mice compared with cycling controls. CONCLUSIONS: These results suggest that follicle depletion and increased gonadotropin levels can alter the histology and the rate of growth of ovarian tumors.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".