Antiviral innate immune response of RNA interference
Bibliographic record
Abstract
RNA interference (RNAi) is an ancient, natural process conserved among species from different kingdoms. RNAi is a transcriptional and post-transcriptional gene silencing mechanism in which, double-stranded RNA or hairpin RNA is cleaved by an RNase III-type enzyme called Dicer into small interfering RNA duplex. This subsequently directs sequence-specific, homology dependent, Watson-Crick base-pairing post-transcriptional gene silencing by binding to its complementary RNA and initiating its elimination through degradation or by persuading translational inhibition. In plants, worms, and insects, RNAi is the main and strong antiviral defense mechanism. It is clear that RNAi silencing, contributes in restriction of viral infection in vertebrates. In a short period, RNAi has progressed to become a significant experimental tool for the analysis of gene function and target validation in mammalian systems. In addition, RNA silencing has then been found to be involved in translational repression, transcriptional inhibition, and DNA degradation. RNAi machinery required for robust RNAi-mediated antiviral response are conserved throughout evolution in mammals and plays a crucial role in antiviral defense of invertebrates, but despite these important functions RNAi contribution to mammalian antiviral innate immune defense has been underestimated and disputed. In this article, we review the literature concerning the roles of RNAi as components of innate immune system in mammals and how, the RNAi is currently one of the most hopeful new advances toward disease therapy. This review highlights the potential of RNAi as a therapeutic strategy for viral infection and gene regulation to modulate host immune response to viral 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".