Strategies and methods to study sex differences in cardiovascular structure and function: a guide for basic scientists
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease remains the primary cause of death worldwide. In the US, deaths due to cardiovascular disease for women exceed those of men. While cultural and psychosocial factors such as education, economic status, marital status and access to healthcare contribute to sex differences in adverse outcomes, physiological and molecular bases of differences between women and men that contribute to development of cardiovascular disease and response to therapy remain underexplored. METHODS: This article describes concepts, methods and procedures to assist in the design of animal and tissue/cell based studies of sex differences in cardiovascular structure, function and models of disease. RESULTS: To address knowledge gaps, study designs must incorporate appropriate experimental material including species/strain characteristics, sex and hormonal status. Determining whether a sex difference exists in a trait must take into account the reproductive status and history of the animal including those used for tissue (cell) harvest, such as the presence of gonadal steroids at the time of testing, during development or number of pregnancies. When selecting the type of experimental animal, additional consideration should be given to diet requirements (soy or plant based influencing consumption of phytoestrogen), lifespan, frequency of estrous cycle in females, and ability to investigate developmental or environmental components of disease modulation. Stress imposed by disruption of sleep/wake cycles, patterns of social interaction (or degree of social isolation), or handling may influence adrenal hormones that interact with pathways activated by the sex steroid hormones. Care must be given to selection of hormonal treatment and route of administration. CONCLUSIONS: Accounting for sex in the design and interpretation of studies including pharmacological effects of drugs is essential to increase the foundation of basic knowledge upon which to build translational approaches to prevent, diagnose and treat cardiovascular diseases in humans.
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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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.021 |
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".