Connective-Tissue Growth Factor Contributes to TGF-β1–induced Lung Fibrosis
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis is a fatal lung disease characterized by progressive and excessive accumulation of myofibroblasts and in the lung. Connective-tissue growth factor (CTGF) exacerbates pulmonary fibrosis in radiation-induced lung fibrosis, and in this study, we demonstrate upregulation of CTGF in a rat lung fibrosis model induced by an adenovirus vector encoding active TGF-β1 (AdTGF-β1). We show that CTGF is also upregulated in patients with idiopathic pulmonary fibrosis. Expression of CTGF was upregulated in vascular smooth muscle cells cultured from fibrotic lungs on Days 7 and 14 as well as endothelial cells sorted from fibrotic lungs on Days 14 and 28. These findings suggest contributions of different cells in maintaining the fibrotic phenotype during fibrogenesis. Treatment of fibroblasts with recombinant CTGF along with TGF-β increases profibrotic markers in fibroblasts, confirming the synergistic effect of recombinant CTGF with TGF-β in inducing pulmonary fibrosis. Also, the fibrotic extracellular matrix upregulated CTGF expression, compared with the normal extracellular matrix, suggesting that not only profibrotic mediators but also a profibrotic environment contributes to fibrogenesis. We also showed that pamrevlumab, a CTGF inhibitory antibody, partially attenuates fibrosis in the model. These results suggest that pamrevlumab could be an option for treatment 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) | 0.001 | 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".