The increased permeability and inflammatory response of porcine intestinal epithelial cells caused by <i>Escherichia coli</i> and <i>Salmonella enterica Typhimurium</i> infections are mitigated by bovine colostrum
Bibliographic record
Abstract
Abstract Gastroenteric infections caused by Escherichia coli and Salmonellaenterica Typhimurium are the source of important losses in porcineindustry. The use of antibiotics in piglets’ food to improve post-weaning performances and prevent enteric infections is raising many concerns, leading the governments to call for finding alternatives that contribute to enhance gut health and resistance to infections. Bovine colostrum is loaded with bioactive pro-immune components that could mediate anti-microbial activities. Therefore, we tested the effect of complete defatted bovine colostrum (BC), as well as bovineserocolostrum and casein fractions (SC and CAS respectively) on porcineintestinal epithelial cell (IPEC J2) in response to the two enteric pathogens. Bothpathogens increased the cell monolayer permeability 4h following the infection, as determined by TEER assays. BC treatment prevented the monolayer disruption caused by both pathogens, while SC only prevented the one caused by Salmonella. After 2h of incubation, the expression of IL8, IL6, TNFA, CCL20, CXCL2 and CXCL10 was increased by bothpathogens, while IL1B, CCL5 and SAA2 were only induced by Salmonella, as measured by Q-PCR. BC and SC reduced the induction of IL8, CCL20 and CXCL2 genes by E. coli(p<0,05). Both BC and SC reduced the expression of IL6 and CCL20 genes by Salmonella (p<0,05), while only BC reduced the induction of IL8, IL6, CCL20, IL1B, SAA2 (p<0,05) and TNFA (p<0,1). Altogether, these results show that the complete form of bovine colostrum can reduce both E. coli and Salmonella infections of porcineintestinal epithelial cells and could be a good alternative to antibiotics in order to control these infections.
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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) | 0.002 | 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".