Bibliographic record
Abstract
This chapter aims to discuss recent advances of circulating miRNAs as new and promising biomarkers for cardiac disease. The elucidation of miRomes between diseased and normal cardiovascular tissues or between different cardiovascular disease types, stages and grades, gives the chance to identify the miRNAs most probably involved in cardiovascular disease and to establish new diagnostic and prognostic markers. Recent findings suggest that circulating miRNAs may be plasma biomarkers for the diagnosis of lung, colorectal, and prostate cancers. These findings have been also tested for cardiovascular disease. miRNAs are present in human plasma in a remarkably stable form that is protected from endogenous RNase activity. The levels of miRNAs in serum are reproducible and consistent among individuals of the same species. In particular, blood miR-1, miR-133, miR- 208a and miR-499 have been suggested as biomarkers of acute myocardial infarction; miR-208, miR-423-5p and some other miRNAs in the circulation are correlated with heart failure; and miR-122, miR-124 and miR-133 may be used to predict cerebral artery occlusion stroke.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".