Capsaicin Attenuates Lipopolysaccharide-Induced Inflammation and Barrier Dysfunction in Intestinal Porcine Epithelial Cell Line-J2
Bibliographic record
Abstract
Capsaicin is a spicy, highly pungent, colorless, vanilloid compound found in chili peppers with anti-inflammatory, antioxidant, anti-cancer, and analgesic properties. However, the protective effects of capsaicin on the pig intestine during inflammation are yet to be explored. This study investigated the effects of capsaicin on the gut inflammatory response, intestinal epithelial integrity, and gene expression level of nutrient transporters in a model of lipopolysaccharide (LPS)-induced inflammation in non-differentiated intestinal porcine epithelial cell line-J2 (IPEC-J2). The results showed that the pre-treatment of cells with capsaicin (100 μM) significantly decreased the gene expression and secretion of proinflammatory cytokines induced by LPS through Toll-like receptor 4 (TLR4)/NF-κB signaling pathway. In addition, pre-treatment of cells with capsaicin also increased both gene and protein abundance of tight junction proteins. Furthermore, pre-treatment cells with capsaicin significantly increased trans-epithelial electrical resistance (TEER) and decreased permeability of fluorescein isothiocyanate-dextran (FD4) from the apical side to the basolateral side compared with the control (P< 0.05). Additionally, pre-treatment of cells with capsaicin upregulated the mRNA abundance of nutrients transporters such as Na+/glucose cotransporter 1 (SGLT1). These results suggested that capsaicin could attenuate LPS-induced inflammation response through TLR4/NF-κB pathway and improve barrier integrity and glucose absorption.
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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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".