TGF-β, Smad3 and the process of progressive fibrosis
Bibliographic record
Abstract
Transient adenovirus-mediated gene transfer of active TGF-beta1 (transforming growth factor-beta1) induces severe and progressive fibrosis in rodent lung without apparent inflammation. Alternatively, transfer of IL-1beta (interleukin 1beta) induces marked tissue injury and inflammation, which develops into progressive fibrosis, associated with an increase in TGF-beta1 concentrations in lung fluid and tissue. Both vector treatments induce a fibrotic response involving myofibroblasts and progressive matrix deposition starting at the peri-bronchial site of expression and extending over days to involve the entire lung and pleural surface. Administration of the TGF-beta1 vector to the pleural space induces progressive pleural fibrosis, which minimally extends into the lung parenchyma. The mechanisms involved in progressive fibrosis need to account for the limitation of fibrosis to specific organs (lung fibrosis and not liver fibrosis or vice versa) and the lack of effect of anti-inflammatory treatments in regulating progressive fibrosis. TGF-beta1 is a key cytokine in the process of fibrogenesis, using intracellular signalling pathways involving the ALK5 receptor and signalling molecules Smad2 and Smad3. Transient gene transfer of either TGF-beta1 or IL-1beta to Smad3-null mouse lung provides little evidence of progressive fibrosis and no fibrogenesis-associated genes are induced. These results suggest that mechanisms of progressive fibrosis involve factors presented within the context of the matrix that define the microenvironment for progressive matrix deposition.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".