Mechanical strain increases cytokine and chemokine production in bronchial fibroblasts from asthmatic patients
Bibliographic record
Abstract
BACKGROUND: Mechanical strain and cytokine stimulation are two important mechanisms leading to airway remodeling in asthma. The effect of mechanical strain on cytokine secretion in airway fibroblasts is not known. The aim of this study was to determine whether bronchial and nasal fibroblasts differentially alter cytokine secretion in response to mechanical strain. METHODS: We measured secretion of the pro-fibrotic cytokine, interleukin-6 (IL-6), and the pro-inflammatory cytokines, IL-8 and monocyte chemotactic protein 1, before and after mechanical strain in bronchial fibroblasts obtained from asthmatic patients [asthmatic bronchial fibroblasts (BAF)] and normal volunteers [normal bronchial fibroblasts (BNF)], and in nasal fibroblasts (NF) obtained from nasal polyps. Cells were grown on flexible membranes and a mechanical strain of 30% amplitude, 1 Hz frequency was applied for 3, 6 and 24 h. Control cells were unstrained. IL-6, IL-8 and monocyte chemotactic protein 1 was measured after 24 h strain using enzyme-linked immunoassay; mRNA was measured by real time polymerase chain reaction. We also measured mRNA for versican, a matrix proteoglycan, known to be upregulated in the asthmatic airway wall. RESULTS: In unstrained conditions, no differences in cytokine secretion were observed. After 24 h strain, BAF secreted more IL-6 than BNF. Mechanical strain increased IL-8 mRNA in BAF, BNF and NF; and IL-6 and versican mRNA, in BAF, only. CONCLUSIONS: Cytokine responses to mechanical strain varied in different airway fibroblast populations, and depended on the site of origin, and the underlying inflammatory state. Strain resulted in IL-6 upregulation and increased message for extracellular matrix protein in bronchial fibroblasts from asthmatic patients only, and may reflect these patients' propensity for airway remodeling.
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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".