Epigenetic regulation of sex dimorphism in cardiovascular health
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality, affecting people of all races, ages, and sexes. Substantial sex dimorphism exists in the prevalence, manifestation, and outcomes of CVDs. Understanding the role of sex hormones as well as sex-hormone-independent epigenetic mechanisms could play a crucial role in developing effective and sex-specific cardiovascular therapeutics. Existing research highlights significant disparities in sex hormones, epigenetic regulators, and gene expression related to cardiac health, emphasizing the need for a nuanced understanding of these variations between men and women. Despite these differences, current treatment approaches for CVDs often lack sex-specific considerations. A pivotal shift toward personalized medicine, informed by comprehensive insights into sex-specific DNA methylation, histone modifications, and non-coding RNA dynamics, holds the potential to revolutionize CVD management. By understanding sex-specific epigenetic complexities, independent of sex hormone influence, future cardiovascular research can be tailored to achieve effective diagnostic and therapeutic interventions for both men and women. This review summarizes the current knowledge and gaps in epigenetic mechanisms and sex dimorphism implicated in CVDs.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".