A Novel Non-invasive Method Allowing for Discovery of Pathologically Relevant Proteins From Small Airways
Bibliographic record
Abstract
Abstract BackgroundBreath contains an aerosol of droplet particles, which are formed from the epithelial lining fluid when the small airways close and re-open during inhalation succeeding a full expiration. These particles can be collected by impaction using the PExA® method (Particles in Exhaled Air), and constitute a potential source of biomarkers reflecting pathological processes in the small airways.ObjectiveOur aim was to investigate if PExA method may be useful for discovery of biomarkers that reflect pathology of small airways.Methods10 healthy controls and 20 subjects with asthma, of whom 10 with small airway dysfunction (SAD), were examined using the PExA instrument. The samples were analysed with the SOMAscan proteomics platform (SomaLogic Inc). ResultsTwo hundred-seven proteins were detected in up to 80% of the samples. Nine proteins showed differential abundance in subjects with SAD as compared to healthy controls. Two of these were less abundant (ALDOA4, C4), and seven more abundant (FIGF, SERPINA1, CD93, CCL18, F10, IgM, IL1RAP). sRAGE levels were lower in ex-smokers (n=14) than in never smokers (n=16). Gene Ontology (GO) annotation database analyses revealed that the PEx proteome is enriched in extracellular proteins associated with extracellular exosome-vesicles and innate immunity.ConclusionThe applied analytical method was reproducible and allowed identification of pathologically interesting proteins in PEx samples from asthmatic subjects with small airway dysfunction. The results suggest that PEx based proteomics is an novel and promising approach to study respiratory diseases with small airway involvement.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".