Adenoviral Gene Transfer of Connective Tissue Growth Factor in the Lung Induces Transient Fibrosis
Bibliographic record
Abstract
Connective tissue growth factor (CTGF) is felt to be one of the key profibrotic factors and is a downstream effector molecule mediating the action of transforming growth factor (TGF)-beta1, a cytokine known to induce severe and progressive fibrosis. However, the in vivo fibrogenic effect of isolated CTGF expression is not well described. We used adenoviral gene transfer to transiently overexpress CTGF in rat lungs after intratracheal administration and compared it with transient overexpression of active TGF-beta1 delivered by a similar adenovirus vector. This high expression of CTGF over 6-10 days induced a moderate but reversible fibrosis. We observed an increase of fibronectin, procollagen 1a2, and endogenous CTGF gene expression at 14 days, which suggested an indirect activation by CTGF. Tissue inhibitor of metalloproteinase-1 was weakly and transiently upregulated after CTGF exposure. These same genes were robustly and persistently stimulated by TGF-beta1 from Day 3 to Day 21. This data suggested that CTGF may act as a TGF-beta1 cofactor rather than a direct fibrogenic factor. We demonstrate that CTGF overexpression can initiate fibrogenic activity but likely requires the presence of additional factors, such as tissue inhibitor of metalloproteinase-1, to maintain a nonfibrolytic environment and to cause progression of fibrosis.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".