Microarray Analysis of Uterine Gene Expression in Mouse and Human Pregnancy
Bibliographic record
Abstract
Improved care of infants born prematurely has increased their survival. However, the incidence of preterm labor has not changed. To understand the processes involved in preterm labor, we used oligonucleotide microarrays to study gene expression in murine and human uterus during pregnancy. The induction of enzymes for prostaglandin synthesis was used as a marker for important changes during pregnancy because prostaglandins strongly contribute to both human and murine labor. We identified 504 genes that changed at least 2-fold between d 13.5 and 19.0 in the gravid mouse uterus. In the pregnant human myometrium, we found 478 genes that changed at least 2-fold in either term or preterm labor compared with preterm nonlabor specimens and 77 genes that significantly varied in both preterm and term labor. Patterns of gene regulation within functional groups comparing human preterm and term labor were similar, although the magnitude of change often varied. Surprisingly, few genes that changed significantly throughout pregnancy were the same in the mouse and human. These data suggest that functional progesterone withdrawal in human myometrium may not be the primary mechanism for labor induction, may implicate similar mechanisms for idiopathic preterm and term labor in humans, and may identify novel targets for further study.
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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 | 0.001 |
| 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".