Mechanical stress-induced mast cell degranulation activates TGF-β1 signalling pathway in pulmonary fibrosis
Bibliographic record
Abstract
BACKGROUND: The role of mast cells accumulating in idiopathic pulmonary fibrosis (IPF) lungs is unknown. OBJECTIVES: We investigated the effect of fibrotic extracellular matrix (ECM) on mast cells in experimental and human pulmonary fibrosis. RESULTS: In IPF lungs, mast cell numbers were increased and correlated with disease severity (control vs 60% 90% vs 60% 90% vs FVC<60%, mean difference=-268.6, 95% CI of difference -441.0 to -96.17, p=0.0007). Plasma tryptase levels were increased in IPF and negatively correlated with FVC (control vs FVC<60%, mean difference=-17.12, 95% CI of difference -30.02 to -4.22, p=0.006: correlation curves R=-0.045, p=0.025). In a transforming growth factor (TGF)-β1-induced pulmonary fibrosis model, chymase-positive and tryptase-positive mast cells accumulated in fibrotic lung. Lung tissue was decellularised and reseeded with bone marrow or peritoneum-derived mast cells; cells on fibrotic ECM released more TGF-β1 compared with normal ECM (active TGF-β1: bone marrow-derived mast cell (BMMC)-DL vs BMMC-TGF-β1 p=0.0005, peritoneal mast cell (PMC)-DL vs PMC-TGF-β1 p=0.0003, total TGF-β1: BMMC-DL vs BMMC-TGF-β1 p=0.013, PMC-DL vs PMC-TGF-β1 p=0.001). Mechanical stretch of lungs caused mast cell degranulation; mast cell stabilisers inhibited degranulation (histamine: cont vs doxantrazole p=0.004, β-hexosaminidase: cont vs doxantrazole, mean difference=1.007, 95% CI of difference 0.2700 to 1.744, p=0.007) and TGF-β1 activation (pSmad2/Smad2: cont vs dox p=0.006). Cromoglycate attenuated pulmonary fibrosis in rats (collagen: phosphate-buffered saline (PBS) vs cromoglycate p=0.036, fibrotic area: PBS vs cromoglycate p=0.031). CONCLUSION: This study suggests that mast cells may contribute to the progression of pulmonary fibrosis.
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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 | 0.000 | 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".