Correlations between salivary- and blood-derived gonadal hormone assessments and implications for inclusion of female participants in research studies

Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thus, pregnancy imposes physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality. Despite advances in cardiovascular physiology research, there is still no comprehensive understanding of maternal cardiovascular adaptations in healthy pregnancies. Furthermore, current approaches for the prognosis of cardiovascular complications during pregnancy are limited. Machine learning (ML) offers new and effective tools for investigating mechanisms involved in pregnancy-related cardiovascular complications as well as the development of potential therapies. The main goal of this review is to summarize existing research that uses ML to understand mechanisms of cardiovascular physiology during pregnancy and develop prediction models for clinical application in pregnant patients. We also provide an overview of ML platforms that can be used to comprehensively understand cardiovascular adaptations to pregnancy and discuss the interpretability of ML outcomes, the consequences of model bias, and the importance of ethical consideration in ML use.

Sex as a biological variable for cardiovascular physiology

There have been ongoing efforts by federal agencies and scientific communities since the early 1990s to incorporate sex and/or gender in all aspects of cardiovascular research. Scientific journals provide a critical function as change agents to influence transformation by encouraging submissions for topic areas, and by setting standards and expectations for articles submitted to the journal. As part of ongoing efforts to advance sex and gender in cardiovascular physiology research, the American Journal of Physiology-Heart and Circulatory Physiology recently launched a call for papers on Considering Sex as a Biological Variable. This call was an overwhelming success, resulting in 78 articles published in this collection. This review summarizes the major themes of the collection, including Sex as a Biological Variable Within: Endothelial Cell and Vascular Physiology, Cardiovascular Immunity and Inflammation, Metabolism and Mitochondrial Energy, Extracellular Matrix Turnover and Fibrosis, Neurohormonal Signaling, and Cardiovascular Clinical and Epidemiology Assessments. Several articles also focused on establishing rigor and reproducibility of key physiological measurements involved in cardiovascular health and disease, as well as recommendations and considerations for study design. Combined, these articles summarize our current understanding of sex and gender influences on cardiovascular physiology and pathophysiology and provide insight into future directions needed to further expand our knowledge.

Association Between Estradiol and Human Aggression: A Systematic Review and Meta-Analysis

OBJECTIVE

Although several studies have examined the association between estradiol and human aggression, a consistent understanding of their correlation has yet to be established. This study aimed to investigate this relationship comprehensively.

METHODS

We systematically searched five English databases (PubMed, Web of Science, EMBASE, Cochrane Library, and CINAHL) from their inception to June 5, 2023. Two authors independently screened publications and extracted data based on predefined inclusion and exclusion criteria. Statistical analyses were performed using Review Manager 5.4, and a random-effects model was used to pool the data.

RESULTS

We identified 14 eligible studies comprising data from 1820 participants that met the inclusion criteria. This meta-analysis indicated a positive correlation between estradiol and human aggression, albeit a weak one. The pooled Fisher z value was 0.16 (95% confidence interval = 0.05-0.26; I2 = 73%, p <.00001). Furthermore, we found that participants' sex and age, the measures of aggression, and the literature quality might be sources of heterogeneity.

CONCLUSIONS

Human aggression exhibited a weak positive correlation with estradiol concentration, whereas this relationship was influenced by participants' sex and age, the measure of aggression used, and the quality assessment of the literature. Gaining a better understanding of the association between estradiol and aggression could aid in the identification of populations prone to aggression.