A New Role for Monocytes in Modulating Myometrial Inflammation During Human Labor1
Bibliographic record
Abstract
Here we fully characterize the cytokine profile of laboring human myometrium using Luminex analysis of 48 cytokine proteins, and stereologically quantified infiltration of monocytes and neutrophils into the myometrium. We hypothesized that monocytes can regulate their accumulation in the myometrium by disruption of proinflammatory cytokines to prevent an uncontrolled inflammatory response after labor onset. We isolated primary human myometrial cells (HMCs) from term, nonlaboring myometrial biopsies. Confluent HMCs were cocultured directly with human monocytic (THP-1) or lymphocytic (U937) cells, and with the same cells spatially separated by a membrane insert. After 72 h, HMCs and THP-1 were harvested separately, and RNA was extracted and analyzed by quantitative PCR. Coculture supernatants were collected and analyzed by Luminex assay and ELISA. We found that the laboring human myometrium produces significantly higher amounts of interleukin (IL) 6, IL9, IL18, IL1RA, CCL2, CCL7, CXCL8, CSF3, and tumor necrosis factor alpha, which coincides with the influx of immune cells. The direct contact or presence of THP-1 monocytes (but not U937 cells) significantly decreased CCL2 protein levels and increased IL1RA protein levels secreted by HMCs. This time-dependent decrease of CCL2 was greater with increasing number of monocytes being in direct contact with HMCs. We suggest a novel mechanism by which monocytes are first recruited to the myometrium by multiple cytokines and contribute to the physiologic inflammation of labor. After completing transmigration, activated monocytes disrupt locally established CCL2 gradients (possible by CCR2-mediated consumption) to limit their accumulation in the uterus. This mechanism may serve as a negative feedback loop to control the local inflammation and promote a return to homeostasis.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".