Citral alleviates peptidoglycan‐induced inflammation and disruption of barrier functions in porcine intestinal epithelial cells
Bibliographic record
Abstract
Peptidoglycan (PGN) is a major polymer in bacterial cell walls and may constrain gut functionality and lower intestinal efficiencies in livestock. Citral has been reported to exhibit antibacterial and anti-inflammatory biological activities, improving the gastrointestinal function of swine. However, the protective effect of citral against PGN-elicited cellular responses and possible underlying mechanisms are unknown. In this study, the porcine jejunal epithelial cell line (IPEC-J2) was challenged with PGN from Staphylococcus aureus (S. aureus) or Bacillus subtilis (B. subtilis) to explore PGN-induced inflammatory responses. Our data showed that the inflammatory response stimulated by PGN from harmful bacteria (S. aureus) was more potent than that from commensal bacteria (B. subtilis) in IPEC-J2 cells. Based on the inflammatory model by PGN from S. aureus, it was demonstrated that PGN could significantly induce inflammatory cytokine production and influence nutrient absorption and barrier function in a dose-dependent manner. However, the PGN-mediated immune responses were remarkably suppressed by citral. In addition, citral significantly attenuated the effect of PGN on the intestine nutrient absorption and barrier function. The expression of TLR2 was strongly induced by PGN stimulation, which was suppressed by citral. All data nominated that citral downregulated PGN-induced inflammation via TLR2-mediated activation of the NF-κB signaling pathway in IPEC-J2 cells. Furthermore, the results also indicate that the PGN degradation through the inclusion of enzymes (e.g., muramidase) as well as the inclusion of citral for attenuating inflammation may improve pig gut health and functionality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".