Clinical value of non-coding RNAs in cardiovascular, pulmonary, and muscle diseases
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it