Characterization of Mouse Anti-Crude Peanut Extract IgE Monoclonal Antibodies
Bibliographic record
Abstract
Abstract INTRODUCTION Immediate hypersensitivity reactions to peanuts, an IgE-mediated food allergy, are a major public health concern. For allergic patients, avoidance currently remains the only viable option. Mouse peanut allergy models are used for studying pathogenesis and therapeutics, However, establishing animal models with high IgE antibody titers is difficult. OBJECTIVE Newly developed mouse anti-crude peanut extract (CPE) IgE monoclonal antibodies (mAbs) were evaluated with in-vitro and in-vivo assays used for studying the pathogenesis of and therapeutics against peanut allergies. METHODS and RESULTS Four hybridomas that produce IgE mAbs against CPE were established from BALB/c mice orally administrated CPE with cholera toxin. All mAbs recognized the Ara h1 allergen in CPE by western blot and worked dose dependently in sandwich ELISAs using an anti-IgE capture mAb and biotinylated CPE. However, only three mAbs worked in indirect ELISAs with CPE coated plates, depending on individual epitope specificities. The three mAbs degranulated RBL-2H3 cells and their degrees of activation varied by mAbs and concentration. In female BALB/c mice, two mAbs developed footpad delayed type hypersensitivity, which peaked at 1–2 hours after sensitization and drop in body temperature in anaphylaxis reactions, which peaked at 40–60 minutes after an IV injection of CPE. CONCLUSION Two out of four IgE mAbs can work for both in-vitro and in-vivo peanut allergy studies. Particularly, the mAbs can develop allergic reactions with sensitization alone in mice. This will be especially useful to evaluate therapeutic methods associated with mast cell activation as the mAbs can accelerate studies with no lengthy immunization protocols.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".