MicroRNAs-Based Therapeutic Strategy for Virally Induced Diseases
Bibliographic record
Abstract
MicroRNAs (miRNAs) are endogenous, short, double-stranded and noncoding RNA molecules that have been identified in a variety of organisms and certain viruses. This group of new molecules is transcribed mainly from the introns and/or exons or intergenic regions and plays important regulatory roles in development and gene expression. Mature miRNAs are typically 20-24 nucleotides in length and regulate target mRNAs post transcriptionally by interactions with partially mismatched sequences in the 3'untraslated regions of these messengers. These interactions result in the suppression of translation or degradation of target mRNAs. At the present, although the biological functions of miRNAs are not completely revealed, a growing body of evidence implicates that miRNA pathway is a new mechanism of gene regulation in both normal and diseased conditions and therefore investigation of miRNA biogenesis and function may add new tools for gene functional study and drug development. In this article, we will briefly review the structure, biogenesis and basic mechanism of action of miRNAs identified in higher organisms and viruses and then focus on the recent progress in research for drug development using the miRNA pathway as a strategy. Particularly, we will discuss the advance, challenge and future directions on antiviral drug development using miRNA as a target or a gene silencing tool for the treatment of viral infections.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 0.002 |
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".