TGF-β1 Induces Progressive Pleural Scarring and Subpleural Fibrosis
Bibliographic record
Abstract
Pleural fibrosis is a misunderstood disorder which can cause severe restrictive lung disease with high morbidity and even mortality. The condition can develop in response to a large variety of diseases and tissue injury, among them infectious disease, asbestos, drugs, and radiation therapy. There is no efficient treatment to reverse established pleural fibrosis. TGF-beta1 is suspected, even if not proven, as a key cytokine in this process. In this study, we used adenoviral gene transfer of TGF-beta1 to the pleural mesothelium in rats. We show that local and transient TGF-beta1 overexpression induces homogenous, prolonged, and progressive pleural fibrosis without pleurodesis, associated with severe impairment of pulmonary function. We further demonstrate that pleural fibrosis can expand into the lung parenchyma from the visceral layer, but not into the muscle from the parietal layer. We provide evidence that matrix accumulation and fibrosis within the parenchyma evolved through a process involving "mesothelial-fibroblastoid transformation" and suggest that the pleural mesothelial cell may be an important player involved in the development of the subpleural distribution pattern known to be a hallmark of pulmonary fibrosis. This new model of pleural fibrosis will allow us to better understand the mechanisms of progressive fibrogenesis, and to explore novel antifibrotic therapies in the pleural cavity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".