An investigation of the impact of sublingual immunotherapy in experimental models of food allergy and anaphylaxis
Bibliographic record
Abstract
Food allergy is a potentially life-threatening disease affecting up to 10% of individuals in Western countries. Clinical reactivity to food allergens is primarily mediated by immunoglobulin (Ig) E, with symptoms ranging from mild urticaria to anaphylaxis. Currently, food allergy remains a disease without a cure. Oral immunotherapy (OIT), which involves consuming small amounts of allergen, remains an experimental treatment in Canada, although has been approved by the Food and Drug Administration (FDA) in the United States for treatment of peanut allergy. While efficacious to induce desensitization, OIT is accompanied by a significant rate of adverse effects. Sublingual immunotherapy (SLIT) is a novel route of treatment for food allergy, where small amounts of allergen are placed under the tongue and held for 2-3 minutes. In contrast to OIT, SLIT offers not only treatment efficacy but also promises an excellent safety profile. The first objective of this thesis was to first develop a SLIT regimen in murine models of food allergy where sensitization is carried out either epicutaneously or intragastrically. Secondly, we investigated the efficacy of SLIT in modulating the clinical and humoral responses in prophylactic and semi-therapeutic settings. In the prophylactic setting, where SLIT was administered prior to sensitizing allergen exposures, SLIT-treated mice were completely protected from allergic sensitization including absent production of serum ovalbumin-specific IgE. In the semi-therapeutic setting, where SLIT was administered to mice primed to develop food allergy, it produced a partial protection against food-induced clinical reactivity. This was associated with lower levels of IgE production in comparison to non-treated, allergic mice. Together, this work provides both an optimized SLIT protocol, as well as evidence on the efficacy of SLIT in the treatment of food allergy in murine models. These findings will aid future work investigating the cellular and molecular mechanisms underlying SLIT-induced protection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.011 | 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 teacher head, 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".