Why the hype - What are microRNAs and why do they provide unique investigative, diagnostic, and therapeutic opportunities in veterinary medicine?
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small, non-coding RNAs that regulate gene expression by inhibiting translation or inducing transcript degradation. MiRNAs act as fine-tuning factors that affect the expression of up to 60% of all mammalian protein coding genes. In contrast to proteins, there is widespread conservation of miRNA sequences across species. This conservation strongly suggests that miRNAs appeared early in evolution and have retained their functional importance. Cross-species conservation provides advantages when compiling candidate markers for health and disease compared to protein-based discoveries. This broad utility is accompanied by the emergence of inexpensive sequencing protocols for the identification of all RNAs in a sample (including miRNAs). With the use of miRNA mimics and antagonists, unique research questions can be answered in biological systems with 'cause and effect' methodology. MiRNAs are readily detectable in blood making them attractive candidates as biomarkers for disease. Here, we review their utility as biomarkers and their potential as therapeutic agents or targets to combat disease.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".