From Quiescence to Activation: The Reciprocal Regulation of Ras and Rho Signaling in Hepatic Stellate Cells
Bibliographic record
Abstract
Chronic liver diseases are marked by persistent inflammation and can evolve into liver fibrosis, cirrhosis, and hepatocellular carcinoma. In an affected liver, hepatic stellate cells (HSCs) transition from a quiescent to an activated state and adopt a myofibroblast-like cell phenotype. While these activated cells play a role in supporting liver regeneration, they can also have detrimental effects on liver function as the disease progresses to fibrosis and cirrhosis. These findings highlight the dynamic switching between different signaling pathways involving Ras, Rho GTPases, and Notch signaling. Notably, two specific members of the Ras and Rho GTPases, Eras and Rnd3, are predominantly expressed in quiescent HSCs, while Mras and Rhoc are more abundant in their activated forms. In addition, this study highlights the critical role of cytosolic Notch1 in quiescent HSCs and Rock in activated HSCs. We hypothesize that distinct yet interdependent intracellular signaling networks regulate HSC fate decisions in two key ways: by maintaining HSC quiescence and homeostasis and by facilitating HSC activation, thereby influencing processes such as proliferation, transdifferentiation, and mesenchymal transition. The proposed signaling model, combined with specific methodological tools for maintaining HSCs in a quiescent state, will deepen our understanding of the mechanisms underlying chronic liver disease and may also pave the way for innovative therapies. These therapies could include small molecule drugs targeting Ras- and Rho-dependent pathways.
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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".