A comparative analyses of group 2 innate lymphoid cells in sputum from patients with non-asthmatic eosinophilic bronchitis and allergic asthma
Bibliographic record
Abstract
Background: Non-asthmatic eosinophilic bronchitis (NAEB) is an airway disorder characterized by chronic cough and sputum eosinophilia in the absence of airway hyperresponsiveness. Group 2 innate lymphoid cells (ILC2) are increased in the sputum from NAEB compared to normal healthy controls and correlate with airway eosinophilia1. We have reported that Neuromedin-U (NMU) via ligation of the cognate receptor, NMUR1 mediates rapid activation of ILC2 in asthma2. Objective: To enumerate and perform phenotyping of airway ILC2 in chronic cough patients diagnosed with NAEB. Methods: Patients with chronic cough referred to a specialized clinic at McMaster University with sputum eosinophilia ≥2% were enrolled in the study and grouped as NAEB (n=8) or AA (n=13) based on methacholine PC20. Total, intracellular cytokine and neuropeptide receptor expression in sputum and blood ILC2 were enumerated by flow cytometry. Results: In sputum, total and NMUR1+ ILC2 were significantly greater in NAEB compared with AA despite comparable eosinophil levels; IL-10+ ILC2 trended higher in NAEB and IL-5/13 levels were comparable between groups. No between group differences were observed in blood. Conclusions: In NAEB, neuropeptide mediated activation of airway ILC2 may play a role in driving airway eosinophilia. 1. Zhan C et al., Allergy 2022;77:649-52 2. R. Sehmi et al., Am J Respir Crit Care Med 2022;205: A5011
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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) | 0.002 | 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".