Gene Expression Profiling of Rat Uterus at Different Stages of Parturition
Bibliographic record
Abstract
A fuller understanding of the process of parturition is needed in view of the current lack of efficient treatment for preterm labor. Using DNA microarrays, we have analyzed patterns of uterine gene expression at d 0 and 20 of pregnancy, at term in labor or not in labor, and at 1 d post partum. Of the 8740 genes analyzed, 562 genes undergoing significant changes were grouped into 5 distinct clusters, each containing many genes not previously known to be involved with uterine functions. Cluster 1 genes were up-regulated at labor and encompassed immune defense and immediate early response genes, including transcription factors NGFI-B/nurr77 and estrogen-responsive gene 1. Cluster 3 genes were acutely suppressed at labor and included extracellular matrix products and genes related to hormonal signaling, implying novel intrauterine mechanisms regulating intracellular cyclic GMP and local steroid hormone concentrations. At labor, more genes were suppressed than activated, indicating that, for the process of labor induction, gene suppression is at least equally important as the more extensively studied processes of gene activation. The study also points to the existence of novel uterine signaling pathways, including Wnt/frizzled and receptor activator of nuclear factor-kappaB (RANK) and its ligand, as well as the involvement of novel signaling molecules such as estrogen-responsive gene 1, decay-accelerating factor 1, and ebnerin. The present results provide the basis for further studies that will enlarge our knowledge of the mechanisms underlying labor and parturition under physiological and pathophysiological conditions.
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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".