A comprehensive update of siRNA delivery design strategies for targeted and effective gene silencing in gene therapy and other applications
Bibliographic record
Abstract
INTRODUCTION: RNA interference (RNAi) using small interfering RNA (siRNA) is a promising strategy to control many genetic disorders by targeting the mRNA of underlying genes and degrade it. However, the delivery of siRNA to targeted organs is highly restricted by several intracellular and extracellular barriers. AREAS COVERED: This review discusses various design strategies developed to overcome siRNA delivery obstacles. The applied techniques involve chemical modification, bioconjugation to specific ligands, and carrier-mediated strategies. Nanotechnology-based systems like liposomes, niosomes, solid lipid nanoparticles (SLNs), dendrimers, and polymeric nanoparticles (PNs) are also discussed. EXPERT OPINION: Although the mechanism of siRNA as a gene silencer is well-established, only a few products are available as therapeutics. There is a great need to develop and establish siRNA delivery systems that protects siRNAs and delivers them efficiently to the desired sitesare efficient and capable of targeted delivery. Several diseases are reported to be controlled by siRNA at their early stages. However, their targeted delivery is a daunting challenge.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".