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Prostate-specific antigen screening at low thresholds of men with pathogenic BRCA1/2 variants
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What makes a man unmanly? The global concept of ‘unmanliness’
This paper presents the findings of a multi-national study that led to the development of a new analytical framework in masculinity research—the Global Concept of ‘Unmanliness’ (GCU). Drawing on three key theories—hegemonic masculinity, precarious manhood and masculinity threat, and emasculation—we conducted an innovative study across 15 countries (selected from an initial pool of 62) to examine cultural perceptions of ‘unmanliness.’ Participants provided open-ended responses to identify traits and behaviors considered unmanly within their cultural contexts. By analyzing common themes expressed by young men, we propose the Global Concept of ‘Unmanliness’ as a framework for understanding how societies define and enforce masculinity norms. Furthermore, comparing these findings with the Global Gender Gap Index (GGGI) revealed a key distinction in how ‘unmanliness’ is characterized across different levels of gender emancipation. In countries with high GGGI rankings (e.g., Norway, Ireland, Germany), ‘unmanliness’ is more often associated with physical traits and behaviors linked to femininity (e.g., clothing, makeup). Conversely, in countries with low GGGI rankings (e.g., Pakistan, Morocco, Nigeria), it is more commonly defined by acts such as violence against women. Our study highlights how cultural and structural gender dynamics shape the boundaries of masculinity and offers a new lens for cross-cultural research on gender norms.
Risk prediction score and equation for progression of arterial stiffness using Japanese longitudinal health examination data
The brachial-ankle pulse wave velocity (baPWV) is useful for evaluating arterial stiffness. No longitudinal studies have examined the association between multiple arterial stiffness risk factors and increased baPWV. We sought to identify factors associated with baPWV ≥1400 cm/s within 5 years and create an equation and simple risk score to predict its occurrence, using data from a large-scale Japanese health examination database. Of 10,284 participants aged 30–69 years for whom follow-up data were available over a 5-year period, 3394 men and 2710 women with baseline baPWV<1400 cm/s were analyzed. We used age, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), fasting blood sugar (FBS), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), estimated glomerular filtration rate (eGFR), habitual exercise, habitual drinking, and smoking history as variables. In the multivariate logistic regression analysis, baPWV≥1400 cm/s was associated significantly with age, BMI, SBP, DBP, HR, FBS, and TG in men and age, SBP, DBP, HR, and smoking history in women. A prediction score based on these factors yielded an area under the curve (AUC) for the 5-year incidence of baPWV≥1400 cm/s of 0.68 for men and 0.71 for women. Furthermore, a risk prediction equation for the 5-year incidence of baPWV≥1400 cm/s showed an AUC = 0.71 for men and 0.77 for women. The prediction equation and a simple prediction score are easy to implement clinically. The predictive ability of these scores and equations for arterial stiffness should be validated in prospective studies.
Utilization of plant-based foods for effective prevention of chronic diseases: a longitudinal cohort study
The present study examined optimal dietary patterns of eight plant-based foods for preventing chronic diseases, including hypertension, stroke, myocardial infarction, and diabetes, using data from the China Health and Nutrition Survey (CHNS). We applied generalized estimating equations to assess time-based changes and gender differences, using residual balancing weights to control time-varying confounders, and employed a restricted cubic spline model to explore dose-response relationships by gender. The findings suggested that a high intake of vegetables and whole grains, along with moderate amounts of fruits, fungi and algae, could help reduce the risk of developing these four chronic diseases simultaneously. Additionally, men could benefit from moderate refined grain consumption, while women should consider increasing their intake of nuts and seeds. Our results indicated that adopting a plant-based diet could provide non-linear protective effects against chronic diseases, with the magnitude of this protection varying by gender.
Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives
Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.
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