Identification of interleukin-1β regulated genes in uterine smooth muscle cells
Bibliographic record
Abstract
We analyzed the response of uterine smooth muscle cells to interleukin-1beta (IL-1beta). We first showed that PHM1-31 myometrial cells, our cellular model, are contractile. To determine the molecular mechanisms of uterine smooth muscle cell activation by proinflammatory cytokines, we performed genechip expression array profiling studies of PHM1-31 cells in the absence and the presence of IL-1beta. In total, we identified 198 known genes whose mRNA levels are significantly modulated (> 2.0-fold change) following IL-1beta exposure. We confirmed the expression changes for selected genes by independent mRNA and protein analysis. The group of genes induced by IL-1beta includes transcription factors and inflammatory response genes such as nuclear factor of kappa light polypeptide gene enhancer in B-cells (NFkappaB), pentraxin-related gene (PTX3), and tumor necrosis factor alpha-induced protein 3/A20 (TNFAIP3/A20). We also found up-regulation of chemokines like C-X-C motif ligand 3 (CXCL3) and extracellular matrix remodeling signaling molecules like tenascin C (TNC). Our data suggest that IL-1beta elicits the rapid activation of a cellular network of genes particularly implicated in inflammatory response that may create a cellular environment favorable for myometrial cell contraction. Our results provide novel insights into the mechanisms of uterine smooth muscle cell regulation and possibly infection-induced preterm labor.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".