Neutrophil elastase and myeloperoxidase in airway extracellular vesicles of cystic fibrosis children during pulmonary exacerbations
Bibliographic record
Abstract
Background: Extracellular vesicles (EVs) play critical roles in disease pathogenesis. The aim of this study was to quantify mediators of neutrophilic inflammation within airway EVs during cystic fibrosis(CF) pulmonary exacerbation(PEx). Methods: EVs were isolated from sputum samples collected before and after antibiotic therapy for PEx and characterized according to international guidelines. Western blot analysis of EV protein extracts was used for EV canonical markers CD63, CD9 and flotillin-1(FLOT1), as well as neutrophil elastase(NE) and myeloperoxidase(MPO). EV content of NE and MPO was expressed as NE/FLOT1 and MPO/FLOT1 protein band densities. Results: Sputum samples from 21 CF children were analysed. Nanoparticle tracking analysis showed high concentrations of particles at the size of small EVs, and transmission electron microscopy confirmed typical EV morphology. Median (IQR) NE/FLOT1 increased from 2.46(1.68-5.25) to 6.83(3.89-8.89, p<0.001) and MPO/FLOT1 from 2.30(1.38-4.44) before to 5.76(3.45-6.94, p<0.01) after PEx therapy; EV concentration and size remained unchanged. There was a correlation between the changes in lung function (ppFEV1) and NE/FLOT1 with therapy in all patients (r=0.66, p<0.001) and in those receiving intravenous antibiotics (r=0.94, p<0.0001). Changes in the lung clearance index (LCI) correlated with changes in MPO/FLOT1 (r=0.68, p=0.05). Conclusions: Airways of children with CF contain EVs that carry NE and MPO as cargo. The lower NE and MPO content at the time of PEx and the correlation with pulmonary function suggest both a functional role of EVs in CF airway inflammation and potential as a biomarker to monitor CF lung disease.
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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 | 0.000 | 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) | 0.001 | 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".