Enteral administration of bacteria fermented formula in newborn piglets: A high fidelity model for necrotizing enterocolitis (NEC)
Bibliographic record
Abstract
OBJECTIVE: To develop an animal model which replicates neonatal NEC and characterizes the importance of bacterial fermentation of formula and short chain fatty acids (SCFAs) in its pathogenesis. BACKGROUND: NEC is a severe form of intestinal inflammation in preterm neonates and current models do not reproduce the human condition. METHODS: Three groups of newborn piglets: Formula alone (FO), Bacteria alone (E.coli: BO) and E.coli-fermented formula (FF) were anesthetized, instrumented and underwent post-pyloric injection of formula, bacteria or fermented-formula. SCFA levels were measured by gas chromatography-mass spectrometry. At 6 h bowel appearance was assessed, histologic and molecular analysis of intestine were performed. Gut inflammation (p65 NF-κB, TLR4, TNF-α, IL-1β), apoptosis (cleaved caspase-3, BAX, apoptosis) and tight junction proteins (claudin-2, occludin) were measured. RESULTS: SCFAs were increased in FF. Small bowel from FF piglet's demonstrated inflammation, coagulative necrosis and pneumatosis resembling human NEC. Histologic gut injury (injury score, mast cell activation) were increased by Bacteria, but more severe in FF piglets. Intestinal expression of p65 NF-κB, NF-κB activation, TNF-α and IL-1β were increased in BO and markedly increased in the FF group (P<0.05 vs. FO). Intestine from Bacteria piglets demonstrated increased apoptotic index, pro-apoptotic protein expression and decreased tight junction proteins. These changes were more severe in FF piglets. CONCLUSIONS: Our piglet model demonstrates the findings of NEC in human neonates: systemic acidosis, intestinal inflammation, pneumatosis and portal venous gas. Bacteria alone can initiate intestinal inflammation, injury and apoptosis, but bacterial fermentation of formula generates SCFAs which contribute to the pathogenesis of NEC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".