The potential of dietary nanoparticles to enhance allergenicity of milk proteins: an <i>in vitro</i> investigation
Bibliographic record
Abstract
In recent years, the popularity of dietary nanoparticles (NPs) in the food industry as additives has raised concerns because of the lack of knowledge about potential adverse health outcomes ensuing from the interactions of NPs with components of the food matrix and gastrointestinal system. In this study, we used a transwell culture system that consisted of human colorectal adenocarcinoma (Caco-2) cells in the apical insert and Laboratory of Allergic Diseases 2 mast cells in the basal compartment to study the effect of NPs on milk allergen delivery across the epithelial layer, mast cell responses and signaling between epithelial and mast cells in allergenic inflammation. A library of dietary particles (silicon dioxide NPs, titanium dioxide NPs and silver NPs) that varied in particle size, surface chemistry and crystal structures with or without pre-exposure to milk was used in this investigation. Milk-interacted particles were found to acquire surface corona and increased the bioavailability of milk allergens (casein and β-lactoglobulin) across the intestinal epithelial layer. The signaling between epithelial cells and mast cells resulted in significant changes in the early phase and late-phase activation of the mast cells. This study suggested that antigen challenge in mast cells with the presence of dietary NPs may cause the transition of allergic responses from an immunoglobulin E (IgE)-dependent mechanism to a mixed mechanism (both IgE-dependent and IgE-independent mechanisms).
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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 | 0.001 |
| 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 | 0.000 |
| 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".