Inducing Hepatitis C Virus Resistance After Pig Liver Transplantation—A Proof of Concept of Liver Graft Modification Using Warm Ex Vivo Perfusion
Bibliographic record
Abstract
Normothermic ex vivo liver perfusion (NEVLP) offers the potential to optimize graft function prior to liver transplantation (LT). Hepatitis C virus (HCV) is dependent on the presence of miRNA(microRNA)-122. Miravirsen, a locked-nucleic acid oligonucleotide, sequesters miR-122 and inhibits HCV replication. The aim of this study was to assess the efficacy of delivering miravirsen during NEVLP to inhibit miR-122 function in a pig LT model. Pig livers were treated with miravirsen during NEVLP or cold storage (CS). Miravirsen absorption, miR-122 sequestration, and miR-122 target gene derepression were determined before and after LT. The effect of miravirsen treatment on HCV infection of hepatoma cells was also assessed. NEVLP improved miravirsen uptake versus CS. Significant miR-122 sequestration and miR-122 target gene derepression were seen with NEVLP but not with CS. In vitro data confirmed miravirsen suppression of HCV replication after established infection and prevented HCV infection with pretreatment of cells, analogous to the pretreatment of grafts in the transplant setting. In conclusion, miravirsen delivery during NEVLP is a potential strategy to prevent HCV reinfection after LT. This is the first large-animal study to provide "proof of concept" for using NEVLP to modify and optimize liver grafts for transplantation.
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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 | 0.001 |
| 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".