Clinical value of non-coding RNAs in cardiovascular, pulmonary, and muscle diseases
Bibliographic record
Abstract
Although a majority of the mammalian genome is transcribed to RNA, mounting evidence indicates that only a minor proportion of these transcriptional products are actually translated into proteins. Since the discovery of the first non-coding RNA (ncRNA) in the 1980s, the field has gone on to recognize ncRNAs as important molecular regulators of RNA activity and protein function, knowledge of which has stimulated the expansion of a scientific field that quests to understand the role of ncRNAs in cellular physiology, tissue homeostasis, and human disease. Although our knowledge of these molecules has significantly improved over the years, we have limited understanding of their precise functions, protein interacting partners, and tissue-specific activities. Adding to this complexity, it remains unknown exactly how many ncRNAs there are in existence. The increased use of high-throughput transcriptomics techniques has rapidly expanded the list of ncRNAs, which now includes classical ncRNAs (e.g., ribosomal RNAs and transfer RNAs), microRNAs, and long ncRNAs. In addition, splicing by-products of protein-coding genes and ncRNAs, so-called circular RNAs, are now being investigated. Because there is substantial heterogeneity in the functions of ncRNAs, we have summarized the present state of knowledge regarding the functions of ncRNAs in heart, lungs, and skeletal muscle. This review highlights the pathophysiologic relevance of these ncRNAs in the context of human cardiovascular, pulmonary, and muscle diseases.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".