Bibliographic record
Abstract
Liver fibrosis occurs in most types of chronic liver diseases and is characterized by excessive accumulation of extracellular matrix proteins, leading to disruption of tissue function and eventually organ failure. Transforming growth factor (TGF)-β represents an important pro-fibrogenic factor and aberrant TGF-β action has been implicated in many disease processes of the liver. Endoglin is a TGF-β co-receptor expressed mainly in endothelial cells that has been shown to differentially regulates TGF-β signal transduction by inhibiting ALK5-Smad2/3 signalling and augmenting ALK1-Smad1/5 signalling. Recent reports demonstrating upregulation of endoglin expression in pro-fibrogenic cell types such as scleroderma fibroblasts and hepatic stellate cells have led to studies exploring the potential involvement of this TGF-β co-receptor in organ fibrosis. A recent article by Meurer and colleagues now shows that endoglin expression is increased in transdifferentiating hepatic stellate cells in vitro and in two different models (carbon tetrachloride intoxication and bile duct ligation) of liver fibrosis in vivo. Moreover, they show that endoglin overexpression in hepatic stellate cells is associated with enhanced TGF-β-driven Smad1/5 phosphorylation and α-smooth muscle actin production without altering Smad2/3 signaling. These findings suggest that endoglin may play an important role in hepatic fibrosis by altering the balance of TGF-β signaling via the ALK1-Smad1/5 and ALK-Smad2/3 pathways and raise the possibility that targeting endoglin expression in transdifferentiating hepatic stellate cells may represent a novel therapeutic strategy for the treatment of liver 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".