The roles of different microRNAs in the regulation of cholesterol in viral hepatitis
Bibliographic record
Abstract
Cholesterol plays a significant role in stabilizing lipid or membrane rafts, which are specific cellular membrane structures. Cholesterol is involved in numerous cellular processes, including regulating virus entry into the host cell. Multiple viruses have been shown to rely on cholesterol for virus entry and/or morphogenesis. Research indicates that reprogramming of the host's lipid metabolism is associated with hepatitis B virus (HBV) and hepatitis C virus (HCV) infections in the progression to severe liver disease for viruses that cause chronic hepatitis. Moreover, knowing the precise mode of viral interaction with target cells sheds light on viral pathogenesis and aids in the development of vaccines and therapeutic targets. As a result, the area of cholesterol-lowering therapy is quickly evolving and has many novel antiviral targets and medications. It has been shown that microRNAs (miRNAs) either directly or indirectly target the viral genome, preventing viral replication. Moreover, miRNAs have recently been shown to be strong post-transcriptional regulators of the genes involved in lipid metabolism, particularly those involved in cholesterol homeostasis. As important regulators of lipid homeostasis in several viral infections, miRNAs have recently come to light. In addition, multiple studies demonstrated that during viral infection, miRNAs modulate several enzymes in the mevalonate/cholesterol pathway. As cholesterol metabolism is essential to the life cycle of viral hepatitis and other viruses, a sophisticated understanding of miRNA regulation may contribute to the development of a novel anti-HCV treatment. The mechanisms underlying the effectiveness of miRNAs as cholesterol regulators against viral hepatitis are explored in this review. Video Abstract.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".