Allergic and Anaphylactic Response to Sesame Seeds in Mice: Identification of Ses i 3 and Basic Subunit of 11s Globulins as Allergens
Bibliographic record
Abstract
BACKGROUND: Allergy to sesame seeds is an emerging food allergy of a serious nature due to a high risk of systemic anaphylaxis. Although a mouse model to study sesame anaphylaxis is desirable, currently it is not available. Here, using a transdermal exposure model system, we tested the hypothesis that sesame seed elicits IL-4-associated IgE antibody response with consequent clinical sensitization in mice. METHODS: Groups of BALB/c mice were exposed to sesame seed extract or saline or a control food (vanilla bean extract) by transdermal applications. Systemic IgE, IgG1 and IgG2a antibody responses were examined using preoptimized ELISA. Type 2 and type 1 cytokine responses were evaluated by ex vivo antigen-mediated activation of spleen cells. Clinical response to oral sesame challenge was studied. Western blot and N-terminal amino acid sequence analyses were performed to identify the sesame allergens. RESULTS: Transdermal exposure to sesame elicited robust IgE and IgG1 but very little IgG2a antibody responses. IgE response to transdermal exposure in two high-IgE responder mice strains with disparate MHC confirmed the intrinsic allergenicity of sesame seed. Transdermal sensitization was associated with activation of IL-4 but not IFN-gamma. Furthermore, oral exposure to sesame resulted in clinical signs of systemic anaphylaxis. Western blot and sequence analysis identified four allergens including Ses i 3 and the basic subunit of 11s globulins. CONCLUSION: These data argue that transdermal exposure to sesame seed can result in IL-4 activation, IgE response and clinical sensitization for systemic anaphylaxis.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".