Hepatitis C Virus-Lipid Interplay: Pathogenesis and Clinical Impact
Bibliographic record
Abstract
Hepatitis C virus (HCV) infection represents the major cause of chronic liver disease, leading to a wide range of hepatic diseases, including cirrhosis and hepatocellular carcinoma. It is the leading indication for liver transplantation worldwide. In addition, there is a growing body of evidence concerning the role of HCV in extrahepatic manifestations, including immune-related disorders and metabolic abnormalities, such as insulin resistance and steatosis. HCV depends on its host cells to propagate successfully, and every aspect of the HCV life cycle is closely related to human lipid metabolism. The virus circulates as a lipid-rich particle, entering the hepatocyte via lipoprotein cell receptors. It has also been shown to upregulate lipid biosynthesis and impair lipid degradation, resulting in significant intracellular lipid accumulation (steatosis) and circulating hypocholesterolemia. Patients with chronic HCV are at increased risk for hepatic steatosis, dyslipidemia, and cardiovascular disease, including accelerated atherosclerosis. This review aims to describe different aspects of the HCV viral life cycle as it impacts host lipoproteins and lipid metabolism. It then discusses the mechanisms of HCV-related hepatic steatosis, hypocholesterolemia, and accelerated atherosclerosis.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".