An intranasally delivered ultra-conserved siRNA prophylactically represses SARS-CoV-2 infection in the lung and nasal cavity.
Bibliographic record
Abstract
There remains a striking overall mortality burden of COVID-19 worldwide. Given the waning effectiveness of current SARS-CoV-2 antivirals due to the rapid emergence of new variants of concern (VOC), we employed a direct-acting molecular therapy approach using gene silencing RNA interference (RNAi) technology. In this study, we developed and screened several ultra-conserved small-interfering RNAs (siRNAs) before selecting one potent siRNA candidate for pre-clinical in vivo testing. This non-immunostimulatory, anti-SARS-CoV-2 siRNA candidate maintains its antiviral activity against all tested SARS-CoV-2 VOC and works effectively as a single agent. For the first time, significant antiviral effects in both the lungs and nasal cavities of SARS-CoV-2 infected mice were observed when this siRNA candidate was delivered intranasally (IN) as a prophylactic agent with the aid of lipid nanoparticles (LNPs). Importantly, a pre-exposure prophylactic IN-delivered anti-SARS-CoV-2 siRNA antiviral that can ameliorate viral replication in the nasal cavity could potentially prevent aerosol spread of respiratory viruses. An IN delivery approach would allow for the development of a direct-acting nasal spray approach that could be self-administered prophylactically.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".