Surfactants Enhance the Tight-Junction Permeability of Food Allergens in Human Intestinal Epithelial Caco-2 Cells
Bibliographic record
Abstract
BACKGROUND: Food additives are responsible for certain allergic types of symptoms. Here Caco-2 cell monolayers were used as a model of the intestinal epithelium for the study of the effect of a food grade surfactant. We determined whether or not the presence of a surfactant enhances the transportation of food allergens across human intestinal epithelial Caco-2 cells. METHODS: This study investigated sucrose monoester fatty acids, which are a food grade surfactant. As an in vitro model of human epithelial cells, Caco-2 cells were grown in monolayers and exposed to different doses of the surfactant in conjunction with ovomucoid, a major egg white allergen. The integrity of the monolayer was assessed by measuring transepithelial electrical resistance (TEER). The permeability of tight junctions and transport of the antigen were studied. RESULTS: TEER correlated with the permeability of tight junctions. TEER significantly decreased upon exposure to a surfactant, indicating an increase in ovomucoid permeability without degradation. The surfactant induced shortening in microvilli, actin disbandment and structural separation of tight junctions. The results indicate that food grade surfactants can increase the paracellular uptake of food allergens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".