The Influence of Diet on MicroRNAs that Impact Cardiovascular Disease
Bibliographic record
Abstract
Food quality and nutritional habits strongly influence human health status. Extensive research has been conducted to confirm that foods rich in biologically active nutrients have a positive impact on the onset and development of different pathological processes, including cardiovascular diseases. However, the underlying mechanisms by which dietary compounds regulate cardiovascular function have not yet been fully clarified. A growing number of studies confirm that bioactive food components modulate various signaling pathways which are involved in heart physiology and pathology. Recent evidence indicates that microRNAs (miRNAs), small single-stranded RNA chains with a powerful ability to influence protein expression in the whole organism, have a significant role in the regulation of cardiovascular-related pathways. This review summarizes recent studies dealing with the impact of some biologically active nutrients like polyunsaturated fatty acids (PUFAs), vitamins E and D, dietary fiber, or selenium on the expression of many miRNAs, which are connected with cardiovascular diseases. Current research indicates that the expression levels of many cardiovascular-related miRNAs like miRNA-21, -30 family, -34, -155, or -199 can be altered by foods and dietary supplements in various animal and human disease models. Understanding the dietary modulation of miRNAs represents, therefore, an important field for further research. The acquired knowledge may be used in personalized nutritional prevention of cardiovascular disease or the treatment of cardiovascular disorders.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".