Structural Diversity Repertoire of Gene Silencing Small Interfering RNAs
Bibliographic record
Abstract
Since the discovery of double-stranded (ds) RNA-mediated RNA interference (RNAi) phenomenon in Caenorhabditis elegans, specific gene silencing based upon RNAi mechanism has become a novel biomedical tool that has extended our understanding of cell biology and opened the door to an innovative class of therapeutic agents. To silence genes in mammalian cells, short dsRNA referred to as small interfering RNA (siRNA) is used as an RNAi trigger to avoid nonspecific interferon responses induced by long dsRNAs. An early structure-activity relationship study performed in Drosophila melanogaster embryonic extract suggested the existence of strict siRNA structural design rules to achieve optimal gene silencing. These rules include the presence of a 3' overhang, a fixed duplex length, and structural symmetry, which defined the structure of a classical siRNA. However, several recent studies performed in mammalian cells have hinted that the gene silencing siRNA structure could be much more flexible than that originally proposed. Moreover, many of the nonclassical siRNA structural variants reported improved features over the classical siRNAs, including increased potency, reduced nonspecific responses, and enhanced cellular delivery. In this review, we summarize the recent progress in the development of gene silencing siRNA structural variants and discuss these in light of the flexibility of the RNAi machinery in mammalian cells.
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) | 0.001 | 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) | 0.001 | 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".