Effects of probiotic lactobacilli on inflammatory responses to Escherichia coli in pig intestinal cells
Bibliographic record
Abstract
In the swine industry, the increased awareness of the in-feed use of antibiotics due to the emergence of resistant bacteria led to an intense search for alternative strategies. Thus, to support swine optimal growth performance and gut health, dietary supplementation with probiotics was promisingly used. Here, porcine intestinal cells (IPEC-J2) were used to investigate the potential of probiotics Limosilactobacillus (Lim.) reuteri CRL2222, Lactobacillus (L.) amylovorus CRL2225, and Lactobacillus (L.) johnsonii CRL2229 to modulate innate immune functions and the intestinal barrier against injuries and pro-inflammatory reactions induced by ETEC F4 infection. By using qPCR, IPEC-J2 cell pre-treatment with each probiotic strain showed a significantly attenuated expression level of pro-inflammatory cytokines TNF-α, IL-8, and IL-6 during ETEC F4-induced infection. The gene expression of TLR4-mediated upstream related genes of the NF-κB signaling pathway (MyD88, IRAK-1, TRAF-6, and TAK-1) was significantly inhibited by the probiotic strains, resulting in the attenuation of inflammatory response in IPEC-J2 infected cells. Moreover, it was also revealed that Lim. reuteri CRL2222 and L. johnsonii CRL2229 probiotics increased the expression of zonula occludens 1 and occludin in ETEC F4-infected IPEC-J2 cells, alleviating the injury of epithelial barrier function. Therefore, these probiotics might be able to reduce pro-inflammatory cytokines blocking the NF-κB pathway through TLR4/MyD88 signaling and to prevent IPEC-J2 cells damage by enhancing the expression of tight-junction proteins. These results provide useful information on potential probiotics for the prevention/improvement of intestinal diseases in piglets.
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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) | 0.001 | 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".