Eosinophil peroxidase in sputum represents a unique biomarker of airway eosinophilia
Bibliographic record
Abstract
BACKGROUND: Sputum eosinophilia has been shown to be a predictor of response to anti-eosinophil therapies in patients with airway diseases. However, quantitative cell counts and differentials of sputum are labor intensive. The objective of this study was to validate a novel ELISA-based assay of eosinophil peroxidase (EPX) in sputum as a rapid and reliable marker of airway eosinophils. METHODS: The utility of EPX-based ELISA as an eosinophil-specific assay was achieved through comparisons with sputum eosinophil differential counts in freshly prepared and archived patient samples from a variety of clinical settings. RESULTS: EPX levels in sputum correlated with eosinophil percentage (r(s) = 0.84) in asthma patients with varying degrees of airway eosinophilia. Significantly, unlike assays of other eosinophil granule proteins (e.g., ECP and EDN), which often detect the presence of these proteins even in asthma patients with neutrophilic bronchitis, EPX-based ELISA levels are not increased in this subset of asthma patients or in COPD patients lacking evidence of an airway eosinophilia. Moreover, sputum EPX was a surrogate marker of airway eosinophilia in other patient studies (e.g., allergen inhalation and treatment trials the anti-(IL-5) therapeutic Mepolizumab™). Finally, EPX levels in cytocentrifuged prepared sputum supernatants correlated with those from rapidly prepared noncentrifuged filtrates of sputum (r(s) = 0.94). CONCLUSION AND CLINICAL IMPLICATION: EPX-based ELISA is a valid, reliable, repeatable, and specific surrogate marker of eosinophils and/or eosinophil degranulation in the sputum of respiratory patients. The novel EPX assay is a valid and reproducible eosinophil-specific assay that can potentially be developed into a point-of-care assessment of eosinophil activity in airway secretions.
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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) | 0.000 | 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 | 0.000 |
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".