IgE binding to linear epitopes of Ara h 2 in peanut allergic preschool children undergoing oral Immunotherapy
Bibliographic record
Abstract
BACKGROUND: For patients with peanut allergy, there are currently no methods to predict who will develop sustained unresponsiveness (SU) after oral immunotherapy (OIT). OBJECTIVE: Assess IgE binding to peanut (PN), Ara h 2, and specific linear epitopes of Ara h 2 as predictors of the important clinical parameters: eliciting dose threshold and attainment of SU following OIT. METHODS: Samples and clinical data were collected from children undergoing OIT. PN- and Ara h 2-sIgE were quantified by ImmunoCAP<sup>®</sup> . IgE binding to linear peptides of Ara h 2 and Ara h 6 was measured with peptide microarrays. RESULTS: Values of PN-sIgE correlated with eliciting dose (P = .001) and with a higher likelihood of achieving SU (P < .0001), but these relationships were lost at higher values for PN-sIgE (≥14 kIU for eliciting dose and ≥35 kIU/L for SU). In subjects with PN-sIgE ≥ 14 kIU/L, binding of IgE to epitopes 5 and 6 of Ara h 2 was associated with a lower eliciting dose at baseline challenge (P < .001; P<sub>c</sub> < .02). In subjects with PN-sIgE ≥ 35 kIU/L, a combined model of IgE binding to epitopes 1, 5 and 6 with PN-sIgE was highly predictive of attainment of SU (AUC of 0.86; P = .0067). CONCLUSION: In young patients with peanut allergy, measurement of PN-sIgE and IgE binding to specific linear epitopes of Ara h 2 in baseline samples may allow stratification of patients regarding sensitivity to challenge and outcome of OIT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".