Human ex vivo and in vitro disease models to study food allergy
Bibliographic record
Abstract
Food allergy is a growing global public health concern. As treatment strategies are currently limited to allergen avoidance and emergency interventions, there is an increasing demand for appropriate models of food allergy for the development of new therapeutics. Many models of food allergy rely heavily on the use of animals, and while useful, many are unable to accurately reflect the human system. In order to bridge the gap between in vivo animal models and clinical trials with human patients, human models of food allergy are of great importance. This review will summarize the commonly used human ex vivo and in vitro models of food allergy and highlight their advantages and limitations regarding how accurately they represent the human in vivo system. We will cover biopsy-based systems, precision cut organ slices, and coculture systems as well as organoids and organ-on-a-chip. The availability of appropriate experimental models will allow us to move forward in the field of food allergy research, to search for effective treatment options and to further explore the cause and progression of this disorder.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".