Differential effects of interleukin-1β and transforming growth factor-β1 on the expression of the inflammation-associated protein, ADAMTS-1, in human decidual stromal cells in vitro
Bibliographic record
Abstract
BACKGROUND: The pro-inflammatory cytokine, interleukin-1 beta (IL-1beta) promotes the proteolytic degradation of the extracellular matrix (ECM) of maternal decidua, a critical step in pregnancy that is counterbalanced by the expression of the anti-inflammatory cytokine, transforming growth factor-beta 1 (TGF-beta1). Recently, the inflammation-associated protein, ADAMTS-1, a member of the ADAMTS (A Disintegrin And Metalloproteinase with ThromboSpondin repeats) gene family of metalloproteinases has been assigned a central role in the formation and organization of tissues. In view of these observations, we have hypothesized that ADAMTS-1 contributes to the cytokine-mediated remodelling of decidual ECM. METHODS: The spatiotemporal expression of ADAMTS-1 in human endometrium was examined by immunohistochemistry. A quantitative-competitive (QC)-PCR strategy and western blot analysis was then employed to determine whether IL-1beta and TGF-beta1 regulate ADAMTS-1 mRNA and protein expression levels in primary cultures of stromal cells isolated from first trimester decidua. RESULTS: ADAMTS-1 expression is associated with decidualization of the endometrial stroma in vivo. IL-1beta increased whereas TGF-beta1 decreased ADAMTS-1 mRNA and protein levels in decidual stromal cell cultures in a concentration- and time-dependent manner. These regulatory effects were attenuated by function-perturbing antibodies specific for either cytokine. CONCLUSION: IL-1beta and TGF-beta1 differentially regulate ADAMTS-1 expression in human decidual stromal cells.
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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".