Mast Cell Desensitization in Allergen Immunotherapy
Bibliographic record
Abstract
Allergen immunotherapy (AIT) is the only treatment with disease-transforming potential for allergic disorders. The immunological mechanisms associated with AIT can be divided along time in two phases: short-term, involving mast cell (MC) desensitization; and long-term, with a regulatory T cell (Treg) response with significant reduction of eosinophilia. This regulatory response is induced in about 70% of patients and lasts up to 3 years after AIT cessation. MC desensitization is characteristic of the initial phase of AIT and it is often related to its success. Yet, the molecular mechanisms involved in allergen-specific MC desensitization, or the connection between MC desensitization and the development of a Treg arm, are poorly understood. The major AIT challenges are its long duration, the development of allergic reactions during AIT, and the lack of efficacy in a considerable proportion of patients. Therefore, reaching a better understanding of the immunology of AIT will help to tackle these short-comings and, particularly, to predict responder-patients. In this regard, omics strategies are empowering the identification of predictive and follow-up biomarkers in AIT. Here, we review the immunological mechanisms underlying AIT with a focus on MC desensitization and AIT-induced adverse reactions. Also, we discuss the identification of novel biomarkers with predictive potential that could improve the rational use of AIT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".