Inflammatory mechanisms of preterm labor and emerging anti-inflammatory interventions
Bibliographic record
Abstract
Preterm birth is a major public health concern, requiring a deeper understanding of its underlying inflammatory mechanisms and to develop effective therapeutic strategies. This review explores the complex interaction between inflammation and preterm labor, highlighting the pivotal role of the dysregulation of inflammation in triggering premature delivery. The immunological environment of pregnancy, characterized by a fragile balance of immune tolerance and resistance, is disrupted in preterm labor, leading to a pathological inflammatory response. Feto-maternal infections, among other pro-inflammatory stimuli, trigger the activation of toll-like receptors and the production of pro-inflammatory mediators, promoting uterine contractility and cervical ripening. Emerging anti-inflammatory therapeutics offer promising approaches for the prevention of preterm birth by targeting key inflammatory pathways. From TLR-4 antagonists to chemokine and interleukin receptor antagonists, these interventions aim to modulate the inflammatory environment and prevent adverse pregnancy outcomes. In conclusion, a comprehensive understanding of the inflammatory mechanisms leading to preterm labor is crucial for the development of targeted interventions in hope of reducing the incidence of preterm birth and improving neonatal health outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".