Bibliographic record
Abstract
Cardiovascular disease is one of the major causes of human morbidity and mortality in the world. MicroRNAs (miRNAs) are small RNAs that regulate gene expression and are known to be involved in the pathogenesis of heart diseases, but the translocation phenomenon and the mode of action in mitochondria are largely unknown. Recent mitochondrial proteome analysis unveiled at least 2000 proteins, of which only 13 are made by the mitochondrial genome. There are numerous studies demonstrating the translocation of proteins into the mitochondria and also translocation of ribosomal RNA (viz., 5S rRNA) into mitochondria. Recent studies have suggested that miRNAs contain sequence elements that affect their subcellular localization, particularly nuclear localization. If there are sequence elements that direct miRNAs to the nucleus, it is also possible that similar sequence elements exist to direct miRNAs to the mitochondria. In this review we have summarized most of the miRNAs that have been shown to play an important role in mitochondrial function, either by regulating mitochondrial genes or by regulating nuclear genes that are known to influence mitochondrial function. While the focus of this review is cardiovascular diseases, we also illustrate the role of mitochondrial miRNA (MitomiR) in the initiation and progression of various diseases, including cardiovascular diseases, metabolic diseases, and cancer. Our goal here is to summarize the miRNAs that are localized to the mitochondrial fraction of cells, and how these miRNAs modulate cardiovascular health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".