Nucleic Acid-Based Treatments Against COVID-19: Potential Efficacy of Aptamers and siRNAs
Bibliographic record
Abstract
Despite significant efforts, there are currently no approved treatments for COVID-19. However, biotechnological approaches appear to be promising in the treatment of the disease. Accordingly, nucleic acid-based treatments including aptamers and siRNAs are candidates that might be effective in COVID-19 treatment. Aptamers can hamper entry and replication stages of the SARS-CoV-2 infection, while siRNAs can cleave the viral genomic and subgenomic RNAs to inhibit the viral life cycle and reduce viral loads. As a conjugated molecule, aptamer-siRNA chimeras have proven to be dual-functioning antiviral therapy, acting both as virus-neutralizing and replication-interfering agents as well as being a siRNA targeted delivery approach. Previous successful applications of these compounds against various stages of the pathogenesis of diseases and viral infections, besides their advantages over other alternatives, might provide sufficient rationale for the application of these nucleic acid-based drugs against the SARS-CoV-2. However, none of them are devoid of limitations. Here, the literature was reviewed to assess the plausibility of using aptamers, siRNAs, and aptamer-siRNA chimeras against the SARS-CoV-2 based on their previously established effectiveness, and discussing challenges lie in applying these molecules.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".