Progenitor cell-derived basophils: a novel barcoded passive degranulation assay in allergy diagnosis
Bibliographic record
Abstract
Background: Effector cells assays provide an overall measure of responsiveness to allergen, but the lack of reliable, high-throughput assays limits the clinical utility of this approach. The aim of this study was to develop a high-throughput Basophil Activation Test (BAT), based on human progenitor cell-derived basophils (PCB), and to investigate the role of PCB activation test (PCBAT) in allergy diagnosis. Methods: PCBs were differentiated from CD34+ progenitor cells, and sensitized with sera from subjects sensitized to cat (n=35, 17 subjects clinical reactivity validated), peanut-allergic (n=30, 15 subjects clinical reactivity validated), peanut-sensitized but tolerant subjects (n=13). Sensitized PCBs were then stimulated with a range of concentrations of the corresponding allergens and degranulation was measured using CD63 expression on flow cytometry. Results: Following passive sensitisation of the mature PCB (2D7+/FcεRI+/CD117-/HLADR-) with serum and stimulation with allergen, we saw a dose-dependent increase in CD63 expression which was allergen specific. In subjects sensititsed to cat there was a positive correlation between PCBAT area under curve (AUC) versus specific IgE (sIgE) to cat (p=0.001) and versus airway responsiveness to inhaled cat allergen (p=0.026). There was a significant negative correlation between PCBAT AUC for peanut allergen and response to oral food challenge test to peanut - subjects with higher PCBAT AUC reacted to a lower dose on the oral food challenge to peanut (p=0.001), and had higher sIgE to Ara h 1 (p=0.007). All peanut tolerant subjects showed no reaction to peanut on PCBAT. Conclusion: PCBAT may confer a powerful alternative tool in allergy testing.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".