The Role of FGL2 in the Pathogenesis and Treatment of Hepatitis C Virus Infection
Bibliographic record
Abstract
Chronic hepatitis C virus (HCV) infection is a leading cause of liver disease worldwide and remains the most common indication for liver transplantation. The current standard of care leads to a sustained viral response of roughly 50% of treated patients at best. Furthermore, anti-viral therapy is expensive, prolonged, and associated with serious side-effects. Evidence suggests that a poor response to treatment may be the result of a suppressed anti-viral immunity due to the presence of increased numbers and activity of CD4(+)CD25(+)Foxp3(+) regulatory T cells (Treg cells). We and others have recently identified fibrinogen-like protein 2 (FGL2) as a putative effector of Treg cells, which accounts for their suppressive function through binding to Fc gamma receptors (FcγR). In an experimental model of fulminant viral hepatitis, our laboratory showed that increased plasma levels of FGL2 pre- and post-viral infection were predictive of susceptibility and severity of disease. Moreover, treatment with antibody to FGL2 fully protected susceptible animals from the lethality of the virus, and adoptive transfer of wild-type Treg cells into resistant fgl2-deficient animals accelerated their mortality post-infection. In patients with HCV infection, plasma levels of FGL2 and expression of FGL2 in the liver correlated with the course and severity of the disease. Collectively, these studies suggest that FGL2 may be used as a biomarker to predict disease progression in HCV patients and be a logical target for the development of novel therapeutic approaches for the treatment of patients with HCV infection.
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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) | 0.000 | 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".