The Road Toward Transformative Treatments for Food Allergy
Bibliographic record
Abstract
A series of landmark studies have provided conclusive evidence that the early administration of food allergens dramatically prevents the emergence of food allergy. One of the greatest remaining challenges is whether patients with established food allergy can return to health. This challenge is particularly pressing in the case of allergies against peanut, tree nuts, fish, and shellfish which are lifelong in most patients and may elicit severe reactions. The standard of care for food allergy is allergen avoidance and the timely administration of epinephrine upon accidental exposure. Epinephrine, and other therapeutic options like antihistamines provide acute symptom relief but do not target the underlying pathology of the disease. In principle, any transformative treatment for established food allergy would require the restoration of a homeostatic immunological state. This may be attained through either an active, non-harmful immune response (immunological tolerance) or a lack of a harmful immune response (e.g., anergy), such that subsequent exposures to the allergen do not elicit a clinical reaction. Importantly, such a state must persist beyond the course of the treatment and exert its protective effects permanently. In this review, we will discuss the immunological mechanisms that maintain lifelong food allergies and are, consequently, those which must be dismantled or reprogrammed to instate a clinically non-reactive state. Arguably, the restoration of such a state in the context of an established food allergy would require a reprogramming of the immune response against a given food allergen. We will discuss existing and experimental therapeutic strategies to eliminate IgE reactivity and, lastly, will propose outstanding questions to pave the road to the development of novel, transformative therapeutics in food allergy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".