Transforming growth factor-β and substrate stiffness regulate portal fibroblast activation in culture
Bibliographic record
Abstract
UNLABELLED: Myofibroblasts derived from portal fibroblasts are important fibrogenic cells in the early stages of biliary fibrosis. In contrast to hepatic stellate cells, portal fibroblasts have not been well studied in vitro, and little is known about their myofibroblastic differentiation. In this article we report the isolation and characterization of rat portal fibroblasts in culture. We demonstrate that primary portal fibroblasts undergo differentiation to alpha-smooth muscle actin-expressing myofibroblasts over 10-14 days. Marker analysis comparing portal fibroblasts to hepatic stellate cells demonstrated that these are distinct populations and that staining with elastin and desmin can differentiate between them. Portal fibroblasts expressed elastin at all stages in culture but never expressed desmin, whereas hepatic stellate cells consistently expressed desmin but never elastin. Immunostaining of rat liver tissue confirmed these results in vivo. Characterization of portal fibroblast differentiation in culture demonstrated that these cells required transforming growth factor-beta (TGF-beta): cells remained quiescent in the presence of a TGF-beta receptor kinase inhibitor, whereas exogenous TGF-beta1 enhanced portal fibroblast alpha-smooth muscle actin expression and stress fiber formation. In contrast, platelet-derived growth factor inhibited myofibroblastic differentiation. Portal fibroblasts were also dependent on mechanical tension for myofibroblastic differentiation, and cells cultured on polyacrylamide supports of variable stiffness demonstrated an increasingly myofibroblastic phenotype as stiffness increased. CONCLUSION: Portal fibroblasts are morphologically and functionally distinct from hepatic stellate cells. Portal fibroblast myofibroblastic differentiation can be modeled in culture and requires both TGF-beta and mechanical tension.
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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 | 0.000 |
| 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".