Clinical utility analysis of the Hoxb8 mast cell activation test for the diagnosis of peanut allergy
Bibliographic record
Abstract
Background: Peanut allergy is among the most severe and common food allergies. The diagnosis has a significant impact on the quality of life for patients and their families. An effective management approach depends on accurate, safe, and easily implementable diagnostic methods. We previously developed a cell-based assay using Hoxb8 mast cells (Hoxb8 MCs) aimed at improving clinical allergy diagnosis. In this study we assessed its diagnostic performance by measuring blinded sera from a prospectively enrolled and pre-validated peanut allergy cohort. Methods: Hoxb8 MCs were passively sensitized with sera from peanut-allergic and peanut tolerant children and adolescents (n=96). Degranulation of Hoxb8 MCs was quantified upon stimulation with dose-titrated peanut extract by means of flow cytometry, using CD107a as activation marker. The results from the Hoxb8 mast cell activation test (Hoxb8 MAT) were compared to established diagnostic assays such as the skin prick test (SPT), specific IgE (sIgE) levels, and the basophil activation test (BAT). Additionally, serum samples from BAT non-responders were assessed with the Hoxb8 MAT. Results: Hoxb8 MAT displayed a robust dose-dependent activation to peanut extract, with a cut-off value of ≤5.2% CD107a positive cells. The diagnostic accuracy was highest at allergen concentrations ≥100 ng/ml, with an area under the receiver operating characteristic curve (AUROC) of 0.97, 93% sensitivity, and 96% specificity, outperforming traditional SPT and sIgE tests. When compared to BAT, Hoxb8 MAT exhibited comparable diagnostic efficacy. However, sera from BAT non-responders were accurately classified into allergics and non-allergics by the Hoxb8 MAT. Conclusions: The Hoxb8 MAT demonstrated a very good diagnostic precision in patients prospectively assessed for peanut allergy comparable to the fresh blood based BAT. It also demonstrated its value for accurate classification of BAT non-responders into allergic and non-allergic individuals. Further investigations into its utility in the routine clinical setting are warranted.
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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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".