Comparison of three modified mouse models of adenomyosis based on invagination theory
Bibliographic record
Abstract
In brief: There is no ideal animal model of adenomyosis, which reflects the imperfect understanding of complex human pathogenesis. In this study, we successfully induced adenomyosis in a mouse model via sharp-blunt trauma, which more closely mimics clinical observations in humans than previous models. Abstract: Adenomyosis is a common gynecological disease in women of reproductive. To date, a satisfactory animal model of adenomyosis has not been established. In this study, 51 female mice were divided into four groups: negative control, an abdominal skin incision was made and sutured without uterine injury; puncture, the uterine horn was punctured using a needle; dilation & curettage, a self-made curette was used to simulate D&C; puncture + dilation & curettage, the uterine horn was punctured, and dilation & curettage were performed. The mice were euthanized 2 weeks, 1 month, or 2 months post-surgery, and the uteruses were harvested. Validity was assessed by histopathological examination. The levels of EMT markers were also detected among the groups. The success rate of adenomyosis induction was higher in the Punct + D&C group than in other groups at all three time points. The highest success rate was observed in the Punct + D&C group 2 months post-surgery. Significantly increased VIM expression was observed in ectopic lesions compared with eutopic endometrium in the Punct + D&C group 2 weeks post-surgery. In addition, VIM expression in eutopic endometrium in the Punct + D&C group was significantly higher than that in the sham group 2 months post-surgery. CDH1 expression was downregulated in the Punct + D&C group compared with the sham group at 2 weeks and 2 months post-surgery. In this study, we successfully established a mouse model of adenomyosis based on invagination theory, which is low cost, quick to establish, and does not interfere with hormone secretion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".