Differential Effects of Gonadotropin-Releasing Hormone I and II on the Urokinase-Type Plasminogen Activator/Plasminogen Activator Inhibitor System in Human Decidual Stromal Cells in Vitro
Bibliographic record
Abstract
To date, the factors capable of regulating the coordinate expression of the urokinase-type plasminogen activator (uPA) and its endogenous inhibitor, plasminogen activator inhibitor (PAI-1), at the maternal-fetal interface remain poorly characterized. In these studies we examined the ability of the classical form of gonadotropin-releasing hormone (GnRH) I and the second, mammalian form of this hormone, GnRH II, to regulate uPA and PAI-1 mRNA and protein expression levels in cultures of stromal cells isolated from first trimester decidual tissues using quantitative competitive-PCR and ELISA, respectively. GnRH I and GnRH II increased uPA mRNA and protein expression levels in these primary cell cultures in a dose- and time-dependent manner. In contrast, GnRH I increased, whereas GnRH II decreased PAI-1 mRNA and protein expression levels in these cells. Cetrorelix, a GnRH receptor antagonist, inhibited the regulatory effects of GnRH I, but not GnRH II, on uPA and PAI-1 expression levels in these decidual stromal cell cultures. Taken together, these observations suggest that GnRH I and GnRH II differentially regulate the balance between uPA and PAI-1 expression levels in the human decidua, possibly via distinct receptor-mediated signaling pathways.
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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 | 0.001 | 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".