Macrophage infiltration of the decidua: early event in the labour process
Bibliographic record
Abstract
Introduction Prematurity remains a leading cause of neonatal morbidity and mortality. A better understanding of what triggers spontaneous preterm labour (PTL) may allow the development of novel therapies to arrest the process. Labour is associated with inflammatory processes, but little is known about the nature or timing of these events. We hypothesized that macrophage infiltration of uterine tissues is an early event in the labour process. Method Rat models were used to examine the timing of inflammatory events. Multiple sections of pregnant uteri on day (d) 21, 22 (am and pm), 23 (term labour) and d1 and 4 postpartum were collected. Uteri were also collected from two models of PTL induced by mifepristone (d16 and 19). Macrophages were identified in fixed uterine sections by immunohistochemistry and quantified by image analysis. Results Macrophage numbers were significantly increased in both decidua and myometrium during labour (p<0.05). Importantly decidual macrophage infiltration occurred 12 h prior to labour (d22pm vs d21; p<0.05), while myometrial infiltration occurred during labour (d23 vs d22pm; p<0.05). Macrophages were more abundant (5-fold higher) in the decidua than myometrium. Elevated macrophage numbers were detected in the decidua in both PTL models (p<0.05), with a similar trend in myometrium. Conclusions These studies confirm that decidual inflammatory events precede labour, and occur prior to myometrial macrophage infiltration, supporting a role for the decidua in triggering labour. Intervening in the labour process at an early stage, for example blocking decidual inflammation, may allow the development of a more successful treatment to prevent PTL.
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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 | 0.001 |
| 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".