The impact of sex and gender on heart–brain axis dysfunction: current concepts and novel perspectives
Bibliographic record
Abstract
The heart–brain axis (HBA) recapitulates all the circuits that regulate bidirectional flow of communication between heart and brain. Several mechanisms may underlie the interdependent relationship involving heterogeneous tissues at rest and during specific target organ injury such as myocardial infarction, heart failure, arrhythmia, stroke, mood disorders, or dementia. In-depth translational studies of the HBA dysfunction under single-organ injury should include both male and female animals to develop sex- and gender-oriented prevention, diagnosis, and treatment strategies. Indeed, sex and gender are determining factors as females and males exhibit significant differences in terms of susceptibility to risk factors, age of onset, severity of symptoms, and outcome. Despite most studies having focused on the male population, we have conducted a careful appraisal of the literature investigating HBA in females. In particular, we have (i) analyzed sex-related heart and brain illnesses, (ii) recapitulated the most significant studies simultaneously conducted on cardio- and cerebro-vascular systems in female populations, and (iii) hypothesized future perspectives for the development of a gender-based approach to HBA dysfunction. Although sex- and gender-oriented research is at its infancy, the impact of sex on HBA dysfunction is opening unexpected new avenues for managing the health of female subjects exposed to risk of lifestyle multi-organ disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".