Transfer RNA-derived small RNAs as novel players and biomarkers in cardiovascular disease
Bibliographic record
Abstract
An emerging field in cardiovascular research is the translational investigation of transfer RNA-derived small RNAs (tsRNAs). TsRNAs, a class of small non-coding RNA molecules, have been shown to modulate cellular functions by regulating gene expression post-transcriptionally. They are implicated in diverse pathological conditions, including cancer, cardiovascular disease (CVD), infectious disease, diabetes, neurological disease, and metabolic disorder. Accumulating evidence suggests tsRNAs as important players and biomarkers in CVD. Dysregulated tsRNAs are identified in atherosclerosis, heart failure, hypertension and other types of CVD. Bioinformatics and in vitro experimental analyses reveal that tsRNAs may participate in the regulation of endothelial and inflammatory cell interactions, endothelial cell and vascular smooth muscle cell proliferation and migration, and cardiac metabolism, mitophagy and remodeling, contributing to the pathogenesis of CVD. In addition, altered tsRNAs possess great diagnostic and prognostic potential in CVD. Nevertheless, there are currently no in vivo mechanistic studies using animal models, and the small sizes of reported clinical studies that examined tsRNAs limit their diagnostic and prognostic value. Although of promise, further research is needed to address the utility of tsRNAs in cardiovascular care.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".