Airway remodelling rather than cellular infiltration characterizes both type2 cytokine biomarker‐high and ‐low severe asthma
Bibliographic record
Abstract
Abstract Background The most recognizable phenotype of severe asthma comprises people who are blood eosinophil and FeNO‐high, driven by type 2 (T2) cytokine biology, which responds to targeted biological therapies. However, in many people with severe asthma, these T2 biomarkers are suppressed but poorly controlled asthma persists. The mechanisms driving asthma in the absence of T2 biology are poorly understood. Objectives To explore airway pathology in T2 biomarker‐high and ‐low severe asthma. Methods T2 biomarker‐high severe asthma (T2‐high, n = 17) was compared with biomarker‐intermediate (T2‐intermediate, n = 21) and biomarker‐low (T2‐low, n = 20) severe asthma and healthy controls ( n = 28). Bronchoscopy samples were processed for immunohistochemistry, and sputum for cytokines, PGD 2 and LTE 4 measurements. Results Tissue eosinophil, neutrophil and mast cell counts were similar across severe asthma phenotypes and not increased when compared to healthy controls. In contrast, the remodelling features of airway smooth muscle mass and MUC5AC expression were increased in all asthma groups compared with health, but similar across asthma subgroups. Submucosal glands were increased in T2‐intermediate and T2‐low asthma. In spite of similar tissue cellular inflammation, sputum IL‐4, IL‐5 and CCL26 were increased in T2‐high versus T2‐low asthma, and several further T2‐associated cytokines, PGD 2 and LTE 4 , were increased in T2‐high and T2‐intermediate asthma compared with healthy controls. Conclusions Eosinophilic tissue inflammation within proximal airways is suppressed in T2 biomarker‐high and T2‐low severe asthma, but inflammatory and structural cell activation is present, with sputum T2‐associated cytokines highest in T2 biomarker‐high patients. Airway remodelling persists and may be important for residual disease expression beyond eosinophilic exacerbations. Registered at ClincialTrials.gov : NCT02883530.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".