Tissue inhibitor of matrix metalloproteinase‐3 expression in the mouse uterus during implantation and artificially induced decidualization
Bibliographic record
Abstract
During implantation in mice, tissue inhibitor of matrix metalloproteinases-3 is believed to play a key role in inhibiting matrix metalloproteinase activity associated with embryo invasion and tissue remodeling. The first objective of this study was to quantitatively compare the steady-state mRNA levels of tissue inhibitors of matrix metalloproteinases between segments of the mouse uterus undergoing decidualization compared to those that are not during early pregnancy plus oil-induced decidualization. Steady-state tissue inhibitor of metalloproteinase-3 mRNA levels were significantly greater in implantation compared to interimplantation areas on days 6 and 7 of pregnancy and in stimulated compared to nonstimulated uterine horns at 48 and 72 hr after artificial induction of decidualization. Steady-state tissue inhibitor of metalloproteinase-1 mRNA levels were significantly greater in implantation compared to interimplantation areas on days 5-8 of pregnancy and in stimulated compared to nonstimulated uterine horns at 24, 48, and 72 hr after oil stimulation. Therefore, the steady-state mRNA levels of tissue inhibitors of metalloproteinase-1 and -3 increased in the uterus during decidualization. The second objective of this study was to determine if transforming growth factor-beta1 influences tissue inhibitors of metalloproteinase mRNA concentrations in mouse endometrial stromal cells. As determined by Northern blot analyses, transforming growth factor beta1 significantly increased tissue inhibitors of matrix metalloproteinases-1 and -3 mRNA levels in cultured mouse endometrial stromal cells isolated from uteri sensitized for decidualization. On the other hand, interleukin-1, epidermal growth factor, and leukemia inhibitory factor had no effect. The results of this study further characterize the tissue inhibitor of metalloproteinase expression in the uterus during implantation and artificially induced decidualization and the potential control of their expression in the stroma by transforming growth factor.
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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 | 0.001 |
| 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".