The role of osteoprotegerin (OPG) in fibrosis: its potential as a biomarker and/or biological target for the treatment of fibrotic diseases
Bibliographic record
Abstract
Fibrosis is defined by excessive formation and accumulation of extracellular matrix proteins, produced by myofibroblasts, that supersedes normal wound healing responses to injury and results in progressive architectural remodelling. Fibrosis is often detected in advanced disease stages when an organ is already severely damaged and can no longer function properly. Therefore, there is an urgent need for reliable and easily detectable markers to identify and monitor fibrosis onset and progression as early as possible; this will greatly facilitate the development of novel therapeutic strategies. Osteoprotegerin (OPG), a well-known regulator of bone extracellular matrix and most studied for its role in regulating bone mass, is expressed in various organs and functions as a decoy for receptor activator of nuclear factor kappa-B ligand (RANKL) and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL). Recently, OPG has been linked to fibrosis and fibrogenesis, and has been included in a panel of markers to diagnose liver fibrosis. Multiple studies now suggest that OPG may be a general biomarker suitable for detection of fibrosis and/or monitoring the impact of fibrosis treatment. This review summarizes our current understanding of the role of OPG in fibrosis and will discuss its potential as a biomarker and/or novel therapeutic target for 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".