Oral immunotherapy with immunodominant T‐cell epitope peptides alleviates allergic reactions in a Balb/c mouse model of egg allergy
Bibliographic record
Abstract
BACKGROUND: Allergen-specific T-cell epitopes are obvious targets for immunotherapeutic interventions in allergic disease. T-cell epitope peptides given orally may provide a practical way of inducing tolerance and preventing allergy. OBJECTIVE: This study investigates oral immunotherapy (OIT) with T-cell epitope peptides of the dominant egg-white allergen ovomucoid (Ovm) in a Balb/c mouse model of egg allergy. METHODS: Groups of mice were orally sensitized to Ovm and subsequently administered Ovm T-cell epitopes [single peptide 157-171 (SP) or multiple peptide (157-171)(3) (MP)], followed by oral challenge with Ovm. Outcomes post oral challenge were measured as clinical signs, serum histamine, antibody activity (IgG, IgE, IgG1, IgG2, IgA), cytokines (IL-4, IFN-γ, IL-12p70, IL-10, TGF-β, and IL-17), and T regulatory cells (Tregs). RESULTS: Clinical signs were less frequent in both SP and MP groups (P ≤ 0.05). Specific IgE was less and IgA was more in both groups; however, SP-treated mice had less histamine and IgG1 and more IgG2-related antibodies indicating a bias toward the type-1 response (P ≤ 0.05). Concentration of type-2 cytokine interleukin-4 (IL-4) was significantly less in both groups and IL-12p70 and IL-10 were more in SP-treated mice (P ≤ 0.001). Interferon-γ, IL-17, and TGF-β did not differ significantly. There was significant increase in the percentage of CD4+FOXP3+ and CD4+CD25+ cells in the SP group, indicating the significant role of Tregs in immune regulation. CONCLUSION: In summary, we demonstrated that OIT with SP and MP comprising the immunodominant regions of Ovm was safe and significantly reduced subsequent frequency of allergy to Ovm, and validated potential use of Ovm T-cell epitope as an immunoregulator.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".