Hepatic stellate cells require a stiff environment for myofibroblastic differentiation
Bibliographic record
Abstract
The myofibroblastic differentiation of hepatic stellate cells (HSC) is a critical event in liver fibrosis and is part of the final common pathway to cirrhosis in chronic liver disease from all causes. The molecular mechanisms driving HSC differentiation are not fully understood. Because macroscopic tissue stiffening is a feature of fibrotic disease, we hypothesized that mechanical properties of the underlying matrix are a principal determinant of HSC activation. Primary rat HSC were cultured on inert polyacrylamide supports of variable but precisely defined shear modulus (stiffness) coated with different extracellular matrix proteins or poly-L-lysine. HSC differentiation was determined by cell morphology, immunofluorescence staining, and gene expression. HSC became progressively myofibroblastic as substrate stiffness increased on all coating matrices, including Matrigel. The degree rather than speed of HSC activation correlated with substrate stiffness, with cells cultured on supports of intermediate stiffness adopting stable intermediate phenotypes. Quiescent cells on soft supports were able to undergo myofibroblastic differentiation with exposure to stiff supports. Stiffness-dependent differentiation required adhesion to matrix proteins and the generation of mechanical tension. Transforming growth factor-β treatment enhanced differentiation on stiff supports, but was not required. HSC differentiate to myofibroblasts in vitro primarily as a function of the physical rather than the chemical properties of the substrate. HSC require a mechanically stiff substrate, with adhesion to matrix proteins and the generation of mechanical tension, to differentiate. These findings suggest that alterations in liver stiffness are a key factor driving the progression of 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) | 0.000 | 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) | 0.001 | 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".