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
Cardiovascular disease is among the main causes of morbidity and mortality in developed countries. The pathological process of the heart is associated with altered expression profile of genes that are important for cardiac function. MicroRNAs (miRNAs) have emerged as one of the central players of gene expression regulation. The implications of miRNAs in the pathological process of cardiovascular system have recently been recognized, representing the most rapidly evolving research field. Here, we summarize and analyze the currently available data from our own laboratory and other groups, providing a comprehensive overview of miRNA function in the heart, including a brief introduction of miRNA biology, expression profile of miRNAs in cardiac tissue, role of miRNAs in cardiac hypertrophy and heart failure, the arrhythmogenic potential of miRNAs, the involvement of miRNAs in vascular angiogenesis, and regulation of cardiomyocyte apoptosis by miRNAs. The target genes and signaling pathways linking the miRNAs to cardiovascular disease are highlighted. The applications of miRNA interference technologies for manipulating miRNA expression, stability, and function as new strategies for molecular therapy of human disease are evaluated. Finally, some specific issues related to future directions of the research on miRNAs relevant to cardiovascular disease are pinpointed and speculated.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it