The Role of Redox Signaling in Epigenetics and Cardiovascular Disease
Bibliographic record
Abstract
SIGNIFICANCE: The term epigenetics refers to the changes in the phenotype and gene expression that occur without alterations in the DNA sequence. There is a rapidly growing body of evidence that epigenetic modifications are involved in the pathological mechanisms of many cardiovascular diseases (CVDs), which intersect with many of the pathways involved in oxidative stress. RECENT ADVANCES: Most studies relating epigenetics and human pathologies have focused on cancer. There has been a limited study of epigenetic mechanisms in CVDs. Although CVDs have multiple established genetic and environmental risk factors, these explain only a portion of the total CVD risk. The epigenetic perspective is beginning to shed new light on how the environment influences gene expression and disease susceptibility in CVDs. Known epigenetic changes contributing to CVD include hypomethylation in proliferating vascular smooth muscle cells in atherosclerosis, changes in estrogen receptor-α (ER-α) and ER-β methylation in vascular disease, decreased superoxide dismutase 2 expression in pulmonary hypertension (PH), as well as trimethylation of histones H3K4 and H3K9 in congestive heart failure. CRITICAL ISSUES: In this review, we discuss the epigenetic modifications in CVDs, including atherosclerosis, congestive heart failure, hypertension, and PH, with a focus on altered redox signaling. FUTURE DIRECTIONS: As advances in both the methodology and technology accelerate the study of epigenetic modifications, the critical role they play in CVD is beginning to emerge. A fundamental question in the field of epigenetics is to understand the biochemical mechanisms underlying reactive oxygen species-dependent regulation of epigenetic modification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".