Sputum cytology during late‐phase responses to inhalation challenge with different allergens
Bibliographic record
Abstract
BACKGROUND: In mouse models of allergic asthma, exposure to different allergens can trigger distinct inflammatory subtypes in the airways. We investigated whether this observation extends to humans. METHODS: We compared the frequency of sputum inflammatory subtypes between mild allergic asthma subjects (n = 129) exposed to different allergens in inhalation challenge tests. These tests were performed using a standardized protocol as part of clinical trials of experimental treatments for asthma, prior to drug randomization. Five allergen types were represented: the house dust mites Dermatophagoides pteronyssinus and Dermatophagoides farinae, ragweed, grass, and cat. RESULTS: Of 118 individuals with a sputum sample collected before allergen challenge (baseline), 45 (38%) had paucigranulocytic, 51 (43%) eosinophilic, 11 (9%) neutrophilic, and 11 (9%) mixed granulocytic sputum. Of note, most individuals with baseline paucigranulocytic sputum developed eosinophilic (48%) or mixed granulocytic (43%) sputum 7 hours after allergen challenge, highlighting the dynamic nature of sputum inflammatory subtype in asthma. Overall, there was no difference in the frequency of sputum inflammatory subtypes following challenge with different allergen types. Similar results were observed at 24 hours after allergen challenge. CONCLUSIONS: Unlike reported in mice, in humans the sputum inflammatory subtype observed after an allergen-induced asthma exacerbation is unlikely to be influenced by the type of allergen used.
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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) | 0.001 | 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".