MicroRNA Mimics or Inhibitors as Antiviral Therapeutic Approaches Against COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Coronaviruses, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for the coronavirus disease 2019 (COVID-19) pandemic, present a significant threat to human health by inflicting a wide variety of health complications and even death. While conventional therapeutics often involve administering small molecules to fight viral infections, small non-coding RNA sequences, known as microRNAs (miRNAs/miR-), may present a novel antiviral strategy. We can take advantage of their ability to modulate host-virus interactions through mediating RNA degradation or translational inhibition. Investigations into miRNA and SARS-CoV-2 interactions can reveal novel therapeutic approaches against this virus. The viral genomes of SARS-CoV-2, severe acute respiratory syndrome coronavirus (SARS-CoV), and Middle East respiratory syndrome coronavirus (MERS-CoV) were searched using the Nucleotide Basic Local Alignment Search Tool (BLASTn) for highly similar sequences, to identify potential binding sites for miRNAs hypothesized to play a role in SARS-CoV-2 infection. miRNAs that target angiotensin-converting enzyme 2 (ACE2), the receptor used by SARS-CoV-2 and SARS-CoV for host cell entry, were also predicted. Several relevant miRNAs were identified, and their potential roles in regulating SARS-CoV-2 infections were further assessed. Current treatment options for SARS-CoV-2 are limited and have not generated sufficient evidence on safety and efficacy for treating COVID-19. Therefore, by investigating the interactions between miRNAs and SARS-CoV-2, miRNA-based antiviral therapies, including miRNA mimics and inhibitors, may be developed as an alternative strategy to fight COVID-19.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it