Induction of papillary carcinoma in human ovarian surface epithelial cells using combined genetic elements and peritoneal microenvironment
Bibliographic record
Abstract
Papillary differentiation is one of the most common histological features of ovarian cancer, although the underlying mechanism that leads to such differentiation is not known. We hypothesized that human ovarian surface epithelial cells can be transformed into carcinoma with papillary differentiation by overexpressing HER2/neu in these cells. Mice were injected either subcutaneously or intraperitoneally with two immortalized human ovarian surface epithelial cell lines after enforced expression of HER-2/neu. Mice subcutaneously injected with tumor cells from either the T29Nt or T80Nt developed undifferentiated carcinomas. In contrast, mice injected intraperitoneally with T29Nt cells developed papillary carcinoma, and those injected intraperitoneally with T80Nt cells developed undifferentiated carcinoma. Our results demonstrate that ovarian surface epithelial cells can develop into papillary carcinoma in mice, and that the induction of papillary differentiation depends not only on specific genetic modifications but also on the tumor microenvironment and epithelial cell type from ovary from different patients.
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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".