17β-Estradiol Accelerates Tumor Onset and Decreases Survival in a Transgenic Mouse Model of Ovarian Cancer
Bibliographic record
Abstract
Epithelial ovarian cancer is thought to be derived from the ovarian surface epithelium (OSE) but often goes undetected in the early stages, and as a result, the factors that contribute to its initiation and progression remain poorly understood. Epidemiological studies have suggested that the female steroid hormones are involved in ovarian carcinogenesis and that women who use hormone replacement therapy are at increased risk of developing the disease. A novel transgenic mouse model of ovarian cancer (tgCAG-LS-TAg) was developed to examine the role of the female reproductive steroid hormones [17beta-estradiol (E(2)) and progesterone (P(4))] on the initiation, progression, and pathology of ovarian cancer. The mouse model uses the Cre-LoxP system to induce expression of the simian virus 40 large and small T antigens (SV40 TAg). After targeted induction of the oncogene in the OSE, mice develop poorly differentiated ovarian tumors, tumor dissemination to tissues within the abdominal cavity, and a subset develops hemorrhagic ascites. Treatment with P(4) had no impact on the disease, but E(2) altered the pathophysiology, resulting in an earlier onset of tumors, decreased overall survival time, and a distinctive papillary histology. Normal ovaries collected from mice treated with E(2), but lacking expression of SV40 TAg, displayed an increase in the areas of columnar and hyperplastic OSE cells compared to placebo-treated controls. A better understanding of the mechanisms by which E(2) alters the morphology of normal OSE cells and reduces survival in this mouse model may translate into improved prevention and treatment options for women using hormone replacement therapy.
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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 | 0.001 |
| 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) | 0.002 | 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".