Neutrophil extracellular traps are downregulated by glucocorticosteroids in lungs in an equine model of asthma
Bibliographic record
Abstract
BACKGROUND: Severe neutrophilic asthma is poorly responsive to glucocorticosteroids (GC). Neutrophil extracellular traps (NETs) within the lungs have been associated with the severity of airway obstruction and inflammation in asthma, and were found to be unaffected by GC in vitro. As IL-17 is overexpressed in neutrophilic asthma and contributes to steroid insensitivity in different cell types, we hypothesized that NETs formation in asthmatic airways would be resistant to GC through an IL-17 mediated pathway. METHODS: Six neutrophilic severe asthmatic horses and six healthy controls were studied while being treated with dexamethasone. Lung function, bronchoalveolar lavage fluid (BALF) cytology and NETs formation, as well as the expression of CD11b and CD13 by blood and airway neutrophils were evaluated. The expression of IL-17 and its role in NETs formation were also studied. RESULTS: Airway neutrophils from asthmatic horses, as opposed to blood neutrophils, enhanced NETs formation, which was then decreased by GC. GC also tended to decrease the expression of CD11b in blood neutrophils, but not in airway neutrophils. IL-17 mRNA was increased in BALF cells of asthmatic horses and was unaffected by GC. However, both GC and IL-17 inhibited NETs formation in vitro. CONCLUSION: GC decreased NETs formation in vitro and also in vivo in the lungs of asthmatic horses. However, airway neutrophil activation during asthmatic inflammation was otherwise relatively insensitive to GC. The contribution of IL-17 to these responses requires further study.
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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) | 0.001 | 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".