Decidual Inflammation Drives Chemokine-Mediated Immune Infiltration Contributing to Term Labor
Bibliographic record
Abstract
Abstract Infiltration of maternal peripheral leukocytes into the uterine tissues is a critical event occurring before, during, and after term labor (TL). In this article, we investigate the contribution of uterine smooth muscle (myometrium) and pregnant endometrium (decidua) to the inflammatory process during human TL. We hypothesize that labor-related physiological inflammation is orchestrated by uterine-secreted cytokines, which dually activate the uterine vascular endothelium and maternal leukocytes to promote their adhesion and infiltration into the uterus. Using Luminex and ELISA assays, we examine a full range of cytokines (45 proteins) in media conditioned by primary decidual and myometrial cells from TL and term not in labor (TNL) women. The effect of conditioned media on the activation of human uterine microvascular endothelial cells was measured by qPCR and on peripheral leukocytes by flow cytometry. Transendothelial migration of calcein-labeled primary leukocytes toward media was assessed by fluorometry. Stromal decidual cells secrete significantly higher levels of multiple cytokines compared with myometrial cells (p < 0.05) and significantly more cytokines during TL than TNL. These cytokines activate uterine microvascular endothelial cells through the upregulation of cell adhesion molecule VCAM-1 and peripheral leukocytes by upregulation of CD11b. Furthermore, multiple cytokines secreted from the TL decidua and myometrium significantly increase migration of granulocytes, monocytes, and lymphocytes compared with TNL (p < 0.05), which was blocked by a broad-spectrum chemokine inhibitor (FX125L). These data reveal the critical role for decidual- and myometrial-secreted cytokines in the activation of inflammatory pathways leading to labor. We suggest that these pathways represent targets for therapeutic intervention during 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".