Gender Disparities in Cardiovascular Disease and Their Management: A Review.

Worldwide, cardiovascular diseases (CVDs) are still the primary cause of death, and there are notable differences between sexes when it comes to symptoms/course and treatment. Due to evolving healthcare technologies, significant progress has been made in understanding CVDs. Hence, it is evident that gender disparities exist in the clinical presentation, prevalence, management, outcomes, and risk factors, including biological, behavioral, and sociocultural factors. This narrative review is designed to provide a generalized idea of gender disparities in CVDs. It aims to provide insights to prove the role of hormonal influences, genetic predispositions, and the difference in physiological outcomes owing to different genders. This review explores subtle distinctions in CVD across genders, including changes in structure, biology, and hormones that affect how illness presents and progresses. Lifestyle variables also influence sociocultural factors and gender disparities in risk profiles. Traditional risk factors, diabetes mellitus (DM), cholesterol levels, and smoking may have different weights and relevance in men and women. Moreover, age and other conventional risk variables have distinct effects on gender. Treatment efficacy may be impacted by the expression of gender-specific factors, emphasizing the necessity for customized strategies. Development of CVDs can be delayed or prevented, and its consequences can be lessened with the early identification and effective management of gender-specific factors. More investigation is necessary to clarify complex interactions between structural, biochemical, and hormonal aspects across genders in order to maximize treatment results and reduce the burden of CVDs.

A clinical trial to compare the effects of aerobic training and resistance training on sleep quality and quality of life in older adults with sleep disturbance.

Objectives: To compare the effects of aerobic and resistance training on sleep quality and Quality of Life (QoL) in older adults with sleep disturbance. Material and Methods: An experimental study was conducted on 36 subjects with sleep disturbance of age ≥60 years. The subjects were assessed for sleep quality and quality of life by using Pittsburgh sleep quality index (PSQI) and WHOQOL-BREF questionnaires. Those who scored PSQI>5 were included in the study and were divided into two groups A and B. Both the groups A and B received warm up and cool down with, aerobic training for group A and resistance training for group B, respectively. The training was given for 40-45 minutes a day, 4 days/week for 4 weeks. Base line and post treatment PSQI and WHOQOL-BREF scores were recorded. Results: Repeated measure ANOVA with Post hoc analysis was done using SPSS software version 16.0. Analysis revealed significant improvement (p<0.05) in PSQI scores within both groups but not between the groups. Post hoc analysis for sleep efficiency, sleep onset latency and sleep duration showed better improvement in resistance training group. Further QoL showed no significant difference between groups for all domains except for domain 3, i.e., social relationship which was found to be better in aerobic training group. Conclusion: Resistance and aerobic training improves both quality of sleep and quality of life. But resistance training has better effect on sleep efficiency, sleep onset latency and sleep duration. As both the training has beneficial effects on sleep quality and quality of life. So, either form of training can be used according to the individuals' functional and medical status to improve sleep quality.

Linear antenna array optimization using flower pollination algorithm.

Flower pollination algorithm (FPA) is a new nature-inspired evolutionary algorithm used to solve multi-objective optimization problems. The aim of this paper is to introduce FPA to the electromagnetics and antenna community for the optimization of linear antenna arrays. FPA is applied for the first time to linear array so as to obtain optimized antenna positions in order to achieve an array pattern with minimum side lobe level along with placement of deep nulls in desired directions. Various design examples are presented that illustrate the use of FPA for linear antenna array optimization, and subsequently the results are validated by benchmarking along with results obtained using other state-of-the-art, nature-inspired evolutionary algorithms such as particle swarm optimization, ant colony optimization and cat swarm optimization. The results suggest that in most cases, FPA outperforms the other evolutionary algorithms and at times it yields a similar performance.