Micro<scp>RNA</scp>‐130a inhibits <scp>HCV</scp> replication by restoring the innate immune response
Bibliographic record
Abstract
Hepatitis C virus (HCV) infection is a major cause of chronic hepatitis and hepatocellular carcinoma. Currently pegylated interferon (IFN) combined with ribavirin remains the best therapeutic approach, although patients infected with HCV genotype I may benefit from adding protease inhibitors as 'triple therapy'. MicroRNAs (miRNAs) are endogenous small noncoding RNAs that regulate gene expression and have recently been shown to play an important role in human innate immune response and as an antiviral in chimpanzees. We studied the effect of miR-130a on the HCV replication. We found that miR-130a significantly inhibits HCV replication in both HCV replicon and J6-/JFH1-infected cells. Over expression of miR-130a upregulated the expression of type I IFN (IFN-α/IFN -β), ISG15, USP18 and MxA, which are involved in innate immune response and decreased expression of miR-122, a well-defined miRNA promoting HCV production. In conclusion, miR-130a inhibits HCV replication/production by restoring host innate immune responses and/or downregulating pro-HCV miR-122. miR-130a might be a potential drug target by modulating host innate immune responses to combat HCV infection.
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 | 0.000 | 0.000 |
| 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".