The role of miRNA regulation in fetal cardiomyocytes, cardiac maturation and the risk of heart disease in adults
Bibliographic record
Abstract
Myocardial infarction is a primary contributor towards the global burden of cardiovascular disease. Rather than repairing the existing damage of myocardial infarction, current treatments only address the symptoms of the disease and reducing the risk of a secondary infarction. Cardiac regenerative capacity is dependent on cardiomyocyte proliferation, which concludes soon after birth in humans and precocial species such as sheep. Human fetal cardiac tissue has some ability to repair following tissue damage, whereas a fully matured human heart has minimal capacity for cellular regeneration. This is in contrast to neonatal mice and adult zebrafish hearts, which retain the ability to undergo cardiomyocyte proliferation and can regenerate cardiac tissue after birth. In mice and zebrafish models, microRNAs (miRNAs) have been implicated in the regulation of genes involved in cardiac cell cycle progression and regeneration. However, the significance of miRNA regulation in cardiomyocyte proliferation for humans and other large mammals, where the timing of heart development in relation to birth is similar, remains unclear. miRNAs may be valuable targets for therapies that promote cardiac repair after injury. Therefore, elucidating the role of specific miRNAs in large animals, where heart development closely resembles that of humans, remains vitally important for identifying therapeutic targets that may be translated into clinical practice focused on tissue repair.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".