Antenatal IL-1-dependent inflammation persists postnatally and causes retinal and sub-retinal vasculopathy in progeny
Bibliographic record
Abstract
Antenatal inflammation as seen with chorioamnionitis is harmful to foetal/neonatal organ development including to eyes. Although the major pro-inflammatory cytokine IL-1β participates in retinopathy induced by hyperoxia (a predisposing factor to retinopathy of prematurity), the specific role of antenatal IL-1β associated with preterm birth (PTB) in retinal vasculopathy (independent of hyperoxia) is unknown. Using a murine model of PTB induced with IL-1β injection in utero, we studied consequent retinal and choroidal vascular development; in this process we evaluated the efficacy of IL-1R antagonists. Eyes of foetuses exposed only to IL-1β displayed high levels of pro-inflammatory genes, and a persistent postnatal infiltration of inflammatory cells. This prolonged inflammatory response was associated with: (1) a marked delay in retinal vessel growth; (2) long-lasting thinning of the choroid; and (3) long-term morphological and functional alterations of the retina. Antenatal administration of IL-1R antagonists - 101.10 (a modulator of IL-1R) more so than Kineret (competitive IL-1R antagonist) - prevented all deleterious effects of inflammation. This study unveils a key role for IL-1β, a major mediator of chorioamnionitis, in causing sustained ocular inflammation and perinatal vascular eye injury, and highlights the efficacy of antenatal 101.10 to suppress deleterious inflammation.
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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 | 0.000 | 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 | 0.001 |
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".