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® . 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; Pc < .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 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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".