Eosinophil phenotypes in circulation and airways of asthmatics after inhaled allergen challenge.
Bibliographic record
Abstract
Rationale: Anti-eosinophil therapies improve clinical measurements of asthma in parallel with full depletion of eosinophils in circulation but only partial depletion in airways. We examined eosinophil phenotypes across these compartments and after airway triggers. Methods: Blood and sputum were collected at baseline, and at 7hr and 24hrs after inhaled allergen and diluent challenges. Sputum cells and whole blood was stained for eosinophils (CD45+ CD16- CD15+). Resident (rEOS: CD62L+ CD123-) and inflammatory (iEOS: CD62L- CD123+) eosinophils were expressed as % of total eosinophils. Results: Blood and sputum eosinophils increased at 7hr and 24hrs post-allergen compared to diluent challenge (p<0.05). Frequency of eosinophil sub-groups in blood was CD62L+ CD123-(rEOS) > CD62L+ CD123+ > CD62L-CD123- > CD62L- CD123+ (iEOS), and in sputum was CD62L-CD123- > CD62L- CD123+ (iEOS) > CD62L+ CD123- (rEOS) > CD62L+CD123+. Blood had significantly higher iEOS and rEOS compared to sputum at all time points (p<0.001). Post-allergen proportions of rEOS drifted downwards in blood and upwards in sputum, with opposite trends in iEOS populations. Conclusion: Higher CD62L-CD123+ (iEOS) and lowering of CD62L+CD123- (rEOS) in blood post-allergen reflects a priming and efflux of eosinophils into airways. The high proportion of CD62L-CD123- eosinophils in sputum could simply represent the post-migration and post-activated airway phenotype.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".