Eosinophil Progenitors in Patients With Non-Asthmatic Eosinophilic Bronchitis, Eosinophilic Asthma, and Normal Controls
Bibliographic record
Abstract
Objective This study aims to explore the potential of in situ airway differentiation of eosinophil progenitors (EoPs) and hematopoietic progenitor cells (HPCs) in sputum and peripheral blood from patients with non-asthmatic eosinophilic bronchitis (NAEB), eosinophilic asthma (EA), and healthy controls (HC). Methods Using flow cytometry, we enumerated sputum and blood HPCs and EoPs in patients with NAEB (n=15), EA (n=15), and HC (n=14) at baseline. Patients with NAEB and EA were then treated for 1 month with budesonide (200 μg, bid) or budesonide and formoterol (200/6 μg, bid), respectively. HPCs and EoPs in both compartments were re-evaluated. Results At baseline, NAEB and EA both had significantly greater numbers of sputum but not blood HPCs and EoPs (p<0.05) compared to HC. There were no differences between NAEB and EA. After 1 month of inhaled corticosteroid (ICS) treatment, NAEB patients showed a significant improvement in cough symptoms, but the attenuation of sputum HPC and EoP levels was not significant. Conclusions NAEB patients have increased airway levels of HPCs and EoPs. One-month treatment with ICS did not fully suppress the level of EoPs in NAEB. Controlling in situ airway differentiation of EoPs may control airway eosinophilia and provide long-term resolution of symptoms in NAEB.
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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 | 0.000 |
| 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) | 0.001 | 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".