Amniotic fluid inhibits Toll-like receptor 4 signaling in the fetal and neonatal intestinal epithelium
Bibliographic record
Abstract
The fetal intestinal mucosa is characterized by elevated Toll-like receptor 4 (TLR4) expression, which can lead to the development of necrotizing enterocolitis (NEC)--a devastating inflammatory disease of the premature intestine--upon exposure to microbes. To define endogenous strategies that could reduce TLR4 signaling, we hypothesized that amniotic fluid can inhibit TLR4 signaling within the fetal intestine and attenuate experimental NEC, and we sought to determine the mechanisms involved. We show here that microinjection of amniotic fluid into the fetal (embryonic day 18.5) gastrointestinal tract reduced LPS-mediated signaling within the fetal intestinal mucosa. Amniotic fluid is abundant in EGF, which we show is required for its inhibitory effects on TLR4 signaling via peroxisome proliferator-activated receptor, because inhibition of EGF receptor (EGFR) with cetuximab or EGF-depleted amniotic fluid blocked the inhibitory effects of amniotic fluid on TLR4, whereas amniotic fluid did not prevent TLR4 signaling in EGFR- or peroxisome proliferator-activated receptor γ-deficient enterocytes or in mice deficient in intestinal epithelial EGFR, and purified EGF attenuated the exaggerated intestinal mucosal TLR4 signaling in wild-type mice. Moreover, amniotic fluid-mediated TLR4 inhibition reduced the severity of NEC in mice through EGFR activation. Strikingly, NEC development in both mice and humans was associated with reduced EGFR expression that was restored upon the administration of amniotic fluid in mice or recovery from NEC in humans, suggesting that a lack of amniotic fluid-mediated EGFR signaling could predispose to NEC. These findings may explain the unique susceptibility of premature infants to the development of NEC and offer therapeutic approaches to this devastating disease.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".