MicroRNA Mimics or Inhibitors as Antiviral Therapeutic Approaches Against COVID-19
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.
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) | 0.001 | 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 | 0.001 |
| 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".