Antenatal Suppression of IL-1 Protects against Inflammation-Induced Fetal Injury and Improves Neonatal and Developmental Outcomes in Mice
Bibliographic record
Abstract
Abstract Preterm birth (PTB) is commonly accompanied by in utero fetal inflammation, and existing tocolytic drugs do not target fetal inflammatory injury. Of the candidate proinflammatory mediators, IL-1 appears central and is sufficient to trigger fetal loss. Therefore, we elucidated the effects of antenatal IL-1 exposure on postnatal development and investigated two IL-1 receptor antagonists, the competitive inhibitor anakinra (Kineret) and a potent noncompetitive inhibitor 101.10, for efficacy in blocking IL-1 actions. Antenatal exposure to IL-1β induced Tnfa, Il6, Ccl2, Pghs2, and Mpges1 expression in placenta and fetal membranes, and it elevated amniotic fluid IL-1β, IL-6, IL-8, and PGF2α, resulting in PTB and marked neonatal mortality. Surviving neonates had increased Il1b, Il6, Il8, Il10, Pghs2, Tnfa, and Crp expression in WBCs, elevated plasma levels of IL-1β, IL-6, and IL-8, increased IL-1β, IL-6, and IL-8 in fetal lung, intestine, and brain, and morphological abnormalities: e.g., disrupted lung alveolarization, atrophy of intestinal villus and colon-resident lymphoid follicle, and degeneration and atrophy of brain microvasculature with visual evoked potential anomalies. Late gestation treatment with 101.10 abolished these adverse outcomes, whereas Kineret exerted only modest effects and no benefit for gestation length, neonatal mortality, or placental inflammation. In a LPS-induced model of infection-associated PTB, 101.10 prevented PTB, neonatal mortality, and fetal brain inflammation. There was no substantive deviation in postnatal growth trajectory or adult body morphometry after antenatal 101.10 treatment. The results implicate IL-1 as an important driver of neonatal morbidity in PTB and identify 101.10 as a safe and effective candidate therapeutic.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".