Testing an emerging animal model for use in the allergenicity assessment of food
Bibliographic record
Abstract
The regulatory assessment of novel food includes tests for allergy. The World Health Organization suggests tests in an animal model of allergy despite the lack of a validated model. We aimed to confirm if C3H/HeJ mice would respond to food of high allergenic potential (peanut), but not to food of low allergenic potential (turkey, potato, spinach). In the first study, C3H/HeJ mice were orally treated, once per week for two weeks, with adjuvant and 0 or 2 mg of peanut or turkey. A second study used adjuvant and 0, 0.1, 1 or 2 mg of peanut, potato or spinach. Blood IgE antibodies and spleen interleukin-4 were quantified. Mice treated with 2 mg peanut developed peanut-specific IgE levels which were significantly higher than control mice (p<0.001, n=10/group). Mice treated with 2 mg turkey developed a similar IgE response to turkey (p<0.001, n=10/group). In the second study, allergy was only triggered in one of ten mice treated with 2 mg peanut. Two of ten mice exposed to 1 mg potato had a response. There were no IgE responders to spinach. Spleen cells from both the peanut- and the spinach-treated mice secreted more allergy-promoting interleukin-4 than controls (p<0.01, n=7-24/group). Levels were not modified in potato-treated mice. C3H/HeJ mice developed food allergy markers to peanut. However, the incidence varied between experiments. Some mice developed a similar response to foods with low allergenic potential. Thus, this model may not be appropriate for safety assessment of novel food.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".