MicroRNA: an Emerging Therapeutic Target and Intervention Tool
Bibliographic record
Abstract
MicroRNAs (miRNAs) are a class of short non-coding RNAs with posttranscriptional regulatory functions. To date, more than 600 human miRNAs have been experimentally identified, and estimated to regulate more than one third of cellular messenger RNAs. Accumulating evidence has linked the dysregulated expression patterns of miRNAs to a variety of diseases, such as cancer, neurodegenerative diseases, cardiovascular diseases and viral infections. MiRNAs provide its particular layer of network for gene regulation, thus possessing the great potential both as a novel class of therapeutic targets and as a powerful intervention tool. In this regard, synthetic RNAs that contain the binding sites of miRNA have been shown to work as a "decoy" or "miRNA sponge" to inhibit the function of specific miRNAs. On the other hand, miRNA expression vectors have been used to restore or overexpress specific miRNAs to achieve a long-term effect. Further, double-stranded miRNA mimetics for transient replacement have been experimentally validated. Endogenous precursor miRNAs have also been used as scaffolds for the induction of RNA interference. This article reviews the recent progress on this emerging technology as a powerful tool for gene regulation studies and particularly as a rationale strategy for design of therapeutics.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".