Monocyte Chemoattractant Protein-1 (CCL-2) Integrates Mechanical and Endocrine Signals That Mediate Term and Preterm Labor
Bibliographic record
Abstract
Recent evidence suggests that leukocytes infiltrate uterine tissues at or around the time of parturition, implicating inflammation as a key mechanism of human labor. MCP-1 (also known as C-C chemokine motif ligand 2, CCL-2) is a proinflammatory cytokine that is up-regulated in human myometrium during labor. Myometrium was collected from pregnant rats across gestation and at labor. Total RNA and proteins were subjected to real-time PCR and ELISA, respectively. Ccl-2 gene and protein expression was significantly up-regulated in the gravid rat myometrium before and during labor, which might suggest that it is regulated positively by mechanical stretch of the uterus imposed by the growing fetus and negatively by physiological withdrawal of progesterone (P4). We confirmed in vivo that: 1) administration of P4 receptor antagonist RU486 induced an increase in Ccl-2 mRNA and preterm labor, whereas 2) artificial maintenance of elevated P4 levels at late gestation caused a significant decrease in gene expression and blocked labor; 3) Ccl-2 was elevated specifically in the gravid horn of unilaterally pregnant rats suggesting that mechanical strain imposed by the growing fetus controls its expression in the myometrium; 4) in vitro static mechanical stretch of primary rat myometrial smooth muscle cells (25% elongation) induced a release of Ccl-2 protein, which was repressed by pretreatment with P4 (1 microM); and 5) stretch enhanced their monocyte chemoattractant activity. These data indicate that Ccl-2 protein serves to integrate mechanical and endocrine signals contributing to uterine inflammation and the induction of labor and thus may represent a novel target for therapeutic prevention of preterm labor in humans.
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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".