Bovine milk-derived exosomes attenuate NLRP3 inflammasome and NF-κB signaling in the lung during neonatal necrotizing enterocolitis
Bibliographic record
Abstract
Abstract Purpose Necrotizing enterocolitis (NEC), an inflammatory intestinal disease common in premature infants, has been associated with the development of lung damage. Toll-like receptor 4 has been shown to regulate inflammation in the NEC lungs, however, other important inflammatory mechanisms have not been thoroughly investigated. In addition, we reported that milk-derived exosomes were able to attenuate intestinal injury and inflammation in experimental NEC. This study aims to (i) investigate the role of the NLRP3 inflammasome and NF-κB pathway in regulating lung damage during experimental NEC; and (ii) evaluate the therapeutic potential of bovine milk exosomes in reducing lung inflammation and injury during NEC.Methods NEC was induced by gavage feeding of hyperosmolar formula, hypoxia, and lipopolysaccharide administration in neonatal mice from postnatal days 5–9. Exosomes were obtained by ultracentrifugation of bovine milk and administered during each formula feed.Results The lung of NEC pups showed increased inflammation, tissue damage, NLRP3 inflammasome expression, and NF-κB pathway activation, which were attenuated upon exosome administration.Conclusion Our findings suggest that the lung undergoes significant inflammation and injury following experimental NEC which are attenuated by bovine milk-derived exosomes. This emphasizes the therapeutic potential of exosomes not just on the intestine but also on the lung.
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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".