Mechanical stress-induced mast cell degranulation activates TGFβ-1 signalling pathway in pulmonary fibrosis
Bibliographic record
Abstract
In this study, we investigated the effect of stiffened fibrotic extracellular matrix (ECM) on mast cell degranulation and phenotype changing. To address these questions, we used our unique ex vivo bath model and in vitro lung decelluralization technique. Pulmonary fibrosis was induced in rats by intratracheal injection of TGFβ-1 adenoviral vector. Rats were examined at day 21. For mechanical stretch study, lungs were cut into lung strip and submitted to mechanical stimuli in a tissue bath equipped with a force transducer and servo-controlled arm. Tissue slices were pre-incubated with two different mast cell stabilizing agents, cromoglycate or doxantrazone. The bath solution and lung trips were harvested as samples. For decelluralization experiment, rat peritoneal mast cells (PMC) were reseeded on decelluralized fibrotic or non-fibrotic rat lung tissue and cultured 3 days. The culture media and lung trips were harvested as samples. Mechanical stress induced active TGFβ-1 and histamine release into the bath solution and induced pSmad2/3 expression in fibrotic lung tissues. Both cromoglycate and doxantrazone significantly inhibited histamine and active TGFβ-1 release into the bath solution, and reduced pSmad2/3 in fibrotic lung strip. Interestingly, cell culture media from PMC reseeded on decelluralized fibrotic lung expressed more active TGFβ-1 compared to the media from PMC on normal lung tissue.
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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".