Eugenol attenuates inflammatory response and enhances barrier function during lipopolysaccharide-induced inflammation in the porcine intestinal epithelial cells
Bibliographic record
Abstract
Eugenol (4-allyl-2-methoxyphenol) is an essential oil component, possessing antimicrobial, anti-inflammatory, and antioxidative properties; however, the effect of eugenol on porcine gut inflammation has not yet been investigated. In this study, an in vitro lipopolysaccharide (LPS)-induced inflammation model in porcine intestinal epithelial cells (IPEC-J2) has been set up. Cells were pretreated with 100 μM (16.42 mg/L) eugenol for 2 h followed by 10 μg/mL LPS stimulation for 6 h. Proinflammatory cytokine secretion; reactive oxygen species; gene expression of proinflammatory cytokines, tight junction proteins, and nutrient transporters; the expression and distribution of zonula occludens-1 (ZO-1); transepithelial electrical resistance (TEER); and cell permeability were measured to investigate the effect of eugenol on inflammatory responses and gut barrier function. The results showed that eugenol pretreatment significantly suppressed the LPS-stimulated interleukin-8 level and the mRNA abundance of tumor necrosis factor-α and restored the LPS-stimulated decrease of the mRNA abundance of tight junction proteins, such as ZO-1 and occludin, and the mRNA abundance of nutrient transporters, such as B0 1 system ASC sodium-dependent neutral amino acid exchanger 2, sodium-dependent glucose transporter 1, excitatory amino acid transporter 1, and peptide transporter 1. In addition, eugenol improved the expression and even redistribution of ZO-1 and tended to increase TEER value and maintained the barrier integrity. In conclusion, a low dose of eugenol attenuated inflammatory responses and enhanced selectively permeable barrier function during LPS-induced inflammation in the IPEC-J2 cell line.
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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) | 0.001 | 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".