Quantitative Assessment of Human Endometriotic Tissue Maintenance and Regression in a Noninvasive Mouse Model of Endometriosis
Bibliographic record
Abstract
Endometriosis is a prevalent disease characterized by the estrogen-dependent ectopic growth of endometrial tissue. Most of the current medical therapies consist in inducing a hypoestrogenic state in patients, but these treatments are associated with severe side effects and high recurrence rates. The development of convenient and reliable endometriosis animal models would be instrumental to accelerate the emergence of new therapeutic alternatives. Recently, we developed an improved experimental model for endometriosis, relying on the infection of human endometrial fragments by an adenovirus carrying the green fluorescent protein. Following injection of fluorescent fragments into nude mice, the implantation and growth of endometriotic-like lesions could be followed noninvasively. In the present work, we demonstrate that this model can be used to quantify the size of fluorescent endometriotic lesions by in vivo imaging. To this end, we repeatedly measured lesion size over a 4-week period in mice supplemented or not with estradiol. The model was adequate to confirm previous results showing that estrogen is dispensable for the implantation phase of endometrial tissue, whereas it is required for lesion maintenance. As a proof of concept for inducing regression of established lesions, ganciclovir was used to treat animals implanted with human fluorescent endometrial fragments expressing thymidine kinase. A significant decrease in lesion size was observed by in vivo imaging in ganciclovir-treated mice. Together, the data indicate that the noninvasive animal model described here provides a tool for drug testing and/or gene target validation in endometriosis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".