Minimal-length Synthetic shRNAs Formulated with Lipid Nanoparticles are Potent Inhibitors of Hepatitis C Virus IRES-linked Gene Expression in Mice
Bibliographic record
Abstract
We previously identified short synthetic shRNAs (sshRNAs) that target a conserved hepatitis C virus (HCV) sequence within the internal ribosome entry site (IRES) of HCV and potently inhibit HCV IRES-linked gene expression. To assess in vivo liver delivery and activity, the HCV-directed sshRNA SG220 was formulated into lipid nanoparticles (LNP) and injected i.v. into mice whose livers supported stable HCV IRES-luciferase expression from a liver-specific promoter. After a single injection, RNase protection assays for the sshRNA and (3)H labeling of a lipid component of the nanoparticles showed efficient liver uptake of both components and long-lasting survival of a significant fraction of the sshRNA in the liver. In vivo imaging showed a dose-dependent inhibition of luciferase expression (>90% 1 day after injection of 2.5 mg/kg sshRNA) with t1/2 for recovery of about 3 weeks. These results demonstrate the ability of moderate levels of i.v.-injected, LNP-formulated sshRNAs to be taken up by liver hepatocytes at a level sufficient to substantially suppress gene expression. Suppression is rapid and durable, suggesting that sshRNAs may have promise as therapeutic agents for liver indications.Molecular Therapy-Nucleic Acids (2013) 2, e123; doi:10.1038/mtna.2013.50; published online 17 September 2013.
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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 | 0.001 | 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".