New perspectives on immunological pathways underlying peanut-induced anaphylaxis
Bibliographic record
Abstract
Food-induced anaphylaxis is often a severe allergic reaction characterized by multi-organ dysfunction and a potentially fatal outcome, and accounts for one third to one-half of anaphylactic reactions treated in emergency departments worldwide. Presently, the role of specific effector cells, immunoglobulins and other effector molecules to food-induced anaphylaxis remains to be fully elucidated. To investigate the relative contribution of immunoglobulin-dependent effector pathways to anaphylactic responses to peanut, wild-type and various mutant mice were sensitized with peanut protein and cholera toxin via oral gavage, once weekly for four weeks. Mice were subjected to different cellular depletion and Fc receptor blocking strategies prior to intraperitoneal challenge with peanut one week following the last sensitization. A number of clinical, physiological and immunological outcomes were evaluated. Our data indicate that pathways, other than the classical mast cell-IgE pathway, contribute to the full spectrum of anaphylactic reactions to peanut. We show that, remarkably, the combined deficiency of mast cells and macrophages, but not mast cells and basophils, or single depletion of macrophages or basophils, averted nearly all clinical and physiological signs of anaphylaxis. However, the single deletion of mast cells, basophils or macrophages prevented the most significant clinical outcome, death. Furthermore, our data show that using IgE- and IgG1-deficient mice as well as FcRIII blockade, both IgE and IgG1 signalling are necessary to fully abolish anaphylactic responses to peanut. While mast cell responses occurred via IgE and IgG1, macrophage responses were fully mediated through IgG1. Peanut-induced anaphylaxis is a process that involves the concerted action of multiple immune effector pathways, and thus interventions targeting one single pathway (e.g. mast cell/IgE) may not be sufficient to fully prevent anaphylactic responses.
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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 | 0.002 | 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 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".