Resident and inflammatory eosinophils in early and late onset asthma.
Bibliographic record
Abstract
Introduction: Asthma is a heterogeneous disease: early and late-onset asthma may differ in clinical presentation and underlying pathophysiology. Two eosinophil subtypes have been recently identified (i.e. inflammatory eosinophils (iEOS) and resident eosinophils (rEOS). Aim and objectives: To measure blood eosinophil subtypes in early and late onset asthma, relating them to clinical and functional outcomes to provide insight into disease mechanisms. Methods: Asthmatic patients, categorized into early (EOA, onset < 12 years, n=17) and late (LOA, onset > 40 years, n=23), were recruited at Padua University Hospital along with a group of healthy controls (HC, n=15). Patients underwent clinical evaluation, lung function (spirometry, DLCO), FeNO, blood eosinophils, total and specific IgE measurements. iEOS and rEOS were quantified by flow cytometry: iEOS (Siglec8+CD16-CD62Llow) and rEOS (Siglec8+CD16-CD62Lhigh). Results: EOA and LOA had higher iEOS levels than healthy controls (mean±SD:7±5.4% vs 7.2±6 vs 4.5±4.4, p=0.04). iEOS levels did not differ between EOA and LOA. Correspondingly, rEOS were reduced in EOA and LOA compared to HC (93±5.4% and 92.8±6 vs 95.5±4). Levels of iEOS correlated negatively with FEV1/FVC (p=0.02, r=-0.34) and MEF50 (p=0.03; r=-0.32). No relation linked iEOS to other functional parameters (including FeNO), or to asthma control (ACT score), disease severity graded by GINA steps, or exacerbations in the previous year. Conclusions: Eosinophil subtypes (iEOS and rEOS) do not differ between EOA and LOA. iEOS levels correlate with FEV1/FVC and MEF50, but not with clinical/functional parameters reflecting disease activity. Further studies are required to evaluate their role as asthma biomarkers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".