M1 Macrophages are a Source of IL-1α: A Driver of Progesterone Metabolism and Myometrial Contraction
Bibliographic record
Abstract
Progesterone (P4) withdrawal is the key trigger for labor onset. Labor is a sterile inflammatory process involving monocyte infiltration, differentiation into M1 or M2 macrophages (Macs) and contributing to the inflammatory milieu in the uterus. Premature leukocyte influx may lead to preterm birth. Inflammatory stimuli induce intracellular progesterone (P4) withdrawal in myometrial cells (MYO) through activation of P4 metabolizing enzyme 20alpha-hydroxysteroid dehydrogenase (20α-HSD). We hypothesized that; (1) the pro-inflammatory M1-Macs induce 20α-HSD in MYO, which causes P4 withdrawal and MYO contractility; and (2) IL-1α produced by M1-Macs mediate the effect of M1-Macs on intracellular P4 withdrawal in MYO. Human myometrial biopsies from women in labor (TL) and not in labor (TNL) revealed higher IL-1α in TL, with M1-Macs in TNL expressing more IL-1α than MYO. In-vitro study shows; (1) higher expression of IL-1α in M1-Macs compared to M2-Macs; (2) treatment of MYO with IL-1α or M1-Macs increased 20α-HSD and contractility; and (3) blockade of IL-1α, AP-1 transcription factors, or co-treatment with non-metabolizable progestin; R5020 inhibits these effects. Our findings highlight the role of tissue-resident M1-Macs in regulating intracellular P4 metabolism and suggest that M1-Macs-derived IL-1α may facilitate P4-withdrawal and uterine contractility associated with labor onset.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".