Investigating the regulatory mechanisms of allergen-specific IgG4 production
Bibliographic record
Abstract
Food allergy (FA) is driven by an abnormal type 2 immune response, where allergen-specific IgE antibodies trigger granulocyte activation and allergic reactions. FA affects millions in Canada and is the leading cause of fatal anaphylaxis in Ontario, with no current cure available. Treatments like allergen immunotherapy (AIT) and monoclonal antibodies (Omalizumab and Dupilumab) aim to reduce symptoms but are not curative and require ongoing treatment. Emerging research suggests that IgG4 antibodies, which increase with chronic allergen exposure and AIT, play a protective role by competing with IgE to prevent granulocyte activation and subsequent allergic symptoms, though the underlying mechanisms remain to be fully elucidated. In this study, we present the development and optimization of tools to explore the role of IgG4 in allergic responses. Utilizing CRISPR-Cas9 technology, we demonstrate the ability to genetically engineer B cell receptors to express allergen-specific antibodies in vitro. Additionally, we developed a robust naïve human B cell culture platform to investigate the impact of various cytokines on IgG4 class-switching. Our findings highlight the critical roles of cytokines such as IL-21 and IL-10 in promoting IgG4 production, while IL-4 appears to be non-essential. These novel tools and platforms shall enable a deeper exploration of the mediators driving IgG4 production in the context of food allergy, ultimately advancing our understanding of the disease and facilitating the development of transformative treatments.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".