Bovine colostrum increases the viability and the healing process of IPEC-J2 intestinal porcine epithelial cells while modulating the barrier function and <i>Escherichia coli</i>-mediated inflammatory responses.
Bibliographic record
Abstract
Abstract Piglet gut response to weaning results in impaired intestinal barrier function, increased inflammatory response and susceptibility to enteric diseases. As the use of antibiotics as growth promoters will be restricted in the swine industry in Canada, novel feeding strategies to improve gut health are needed. This study aims to determine the potential of defatted bovine colostrum (BC) and its fractions, the serocolostrum (SC) and caseins (CAS), to promote viability and the healing process and to modulate barrier integrity and the inflammatory response induced by enterotoxigenic E. coli (ETEC) in IPEC-J2 intestinal epithelial porcine cells. To evaluate cell viability and wound healing, XTT cell proliferation and migration scratch assays were performed on IPEC-J2 cells incubated with BC, SC, or CAS. Monolayer permeability of ETEC infected IPEC-J2 cells incubated with BC, SC or CAS was monitored by transepithelial electrical resistance measurements. Inflammatory and oxidative responses were assessed by gene expression analysis of several cytokines and chemokines by qPCR. Incubation with BC derivatives led to 8 to 14% increases IPEC-J2 cell viability, in comparison to untreated cells (P ≤ 0.05). IPEC-J2 cell migration was increased by 42% after SC addition, as opposed to non-treated cells (P ≤ 0.05). BC and SC treatments significantly reduced ETEC-mediated increases in IL-8 and CCL20 expression (P ≤ 0.05). BC treatment also significantly reduced IPEC-J2 cell monolayer permeability induction after ETEC infection. These results show that BC and SC fractions improve intestinal epithelial cell integrity and decrease ETEC-mediated inflammatory responses. Therefore, BC should be considered as a potential alternative to antibiotics.
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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".