Erythropoietin Protects Epithelial Cells from Excessive Autophagy and Apoptosis in Experimental Neonatal Necrotizing Enterocolitis
Bibliographic record
Abstract
Neonatal necrotizing enterocolitis (NEC) is a devastating gastrointestinal disease of preterm infants. Increased intestinal epithelium permeability is an early event in NEC pathogenesis. Autophagy and apoptosis are induced by multiple stress pathways which may impact the intestinal barrier, and they have been associated with pathogenesis of diverse gastrointestinal diseases including inflammatory bowel disease. Using both in vitro and in vivo models, this study investigates autophagy and apoptosis under experimental NEC stresses. Furthermore this study evaluates the effect of erythropoietin (Epo), a component of breast milk previously shown to decrease the incidence of NEC and to preserve intestinal barrier function, on intestinal autophagy and apoptosis. It was found that autophagy and apoptosis are both rapidly up regulated in NEC in vivo as indicated by increased expression of the autophagy markers Beclin 1 and LC3II, and by evidence of apoptosis by TUNEL and cleaved caspase-3 staining. In the rat NEC experimental model, autophagy preceded the onset of apoptosis in intestine. In vitro studies suggested that Epo supplementation significantly decreased both autophagy and apoptosis via the Akt/mTOR signaling pathway and the MAPK/ERK pathway respectively. These results suggest that Epo protects intestinal epithelium from excessive autophagy and apoptosis in experimental NEC.
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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) | 0.001 | 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".