Future Promise of siRNA and Other Nucleic Acid Based Therapeutics for the Treatment of Chronic HCV
Bibliographic record
Abstract
RNA interference (RNAi) is gaining favor as a potential therapeutic option for the treatment of Hepatitis C virus infections. RNAi, first discovered in plants, induces sequence specific degradation of messenger RNA following the introduction of short interference RNA (siRNA). RNAi is a natural defense mechanism used by plants to combat viral infections, and the discovery of RNAi activity in mammalian cells has prompted several drug companies to investigate and exploit RNAi based drugs as a potential therapy against HCV infections. A number of research groups have demonstrated that strong RNAi activity can be induced against HCV using synthetic siRNA duplexes as triggers, or by expressing short hairpin RNAs from plasmid or viral vectors. However, much work remains to improve delivery, maintain specificity and limit the development of virus resistance. HCV is capable of evading RNAi activity through the incorporation escape mutations within the siRNA target sequence, highlighting the importance of implementing strategies to limit the development of resistance. Other nucleic acid based therapies such as antisense oligonucleotides, RNA aptamers and ribozymes have also been considered for use as HCV therapeutics, and we will outline the potential opportunities and obstacles to their use as well as RNAi.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".