Interleukin‐17A and vascular remodelling in severe asthma; lack of evidence for a direct role
Bibliographic record
Abstract
Summary Background Bronchial vascular remodelling may contribute to the severity of airway narrowing through mucosal congestion. Interleukin ( IL )‐17A is associated with the most severe asthmatic phenotype but whether it might contribute to vascular remodelling is uncertain. Objective To assess vascular remodelling in severe asthma and whether IL ‐17A directly or indirectly may cause endothelial cell activation and angiogenesis. Methods Bronchial vascularization was quantified in asthmatic subjects, COPD and healthy subjects together with the number of IL ‐17A + cells as well as the concentration of angiogenic factors in the sputum. The effect of IL ‐17A on in vitro angiogenesis, cell migration and endothelial permeability was assessed directly on primary human lung microvascular endothelial cells ( HMVEC ‐L) or indirectly with conditioned medium derived from normal bronchial epithelial cells ( NHBEC ), fibroblasts ( NHBF ) and airway smooth muscle cells ( ASMC ) after IL ‐17A stimulation. Results Severe asthmatics have increased vascularity compared to the other groups, which correlates positively with the concentrations of angiogenic factors in sputum. Interestingly, we demonstrated that increased bronchial vascularity correlates positively with the number of subepithelial IL ‐17A + cells. However IL ‐17A had no direct effect on HMVEC ‐L function but it enhanced endothelial tube formation and cell migration through the production of angiogenic factors by NHBE and ASMC . Conclusions & Clinical Relevance Our results shed light on the role of IL ‐17A in vascular remodelling, most likely through stimulating the synthesis of other angiogenic factors. Knowledge of these pathways may aid in the identification of new therapeutic targets.
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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) | 0.000 | 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".