Anti-inflammatory effects of epidermal growth factor on the immature human intestine
Bibliographic record
Abstract
The inflammatory response of the preterm infants' intestine underlines its inability to respond to hemodynamic stress, microbes, and nutrients. Recent evidence suggests that exogenous epidermal growth factor (EGF) exerts a therapeutic influence on neonatal enteropathies. However, the molecular mechanisms underlying the beneficial effects of EGF remain to be clarified. The purpose of this study was to evaluate the impact of EGF on the gene expression profiles of the developing human small and large intestine at midgestation in serum-free organ cultures using microarrays. The gene expression profiles of cultured human fetal ileal and colonic explants were investigated in the absence or presence of a physiological concentration of 50 ng/ml EGF for 48 h. Data were analyzed with the Ingenuity Pathway Analysis (IPA) software and confirmed by qPCR. We found a total of 6,474 differentially expressed genes in the two segments in response to EGF. IPA functional analysis revealed that in addition to differentially modulating distinct cellular, molecular, and physiological functions in the small and large intestine, EGF regulated the inflammatory response in both intestinal segments in a distinct manner. For instance, several intestinal-derived chemokines such as CCL2, CCL25, CXCL5, and CXCL10 were found to be differentially regulated by EGF in the immature ileum and colon. The findings showing the anti-inflammatory influence of exogenous EGF suggests a mechanistic basis for the beneficial effects of EGF on neonatal enteropathies. These results reinforce growing evidence that by midgestation, the human small intestine and colon rely on specific and distinct regulatory pathways.
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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 | 0.000 |
| 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".