Menopausal symptoms are associated with deterioration in physical, mental, and sexual health, lowering women’s quality of life (QoL). Our study objective is to examine the effect of exercise on QoL in women with menopausal symptoms. After initially identifying 1306 studies published on PubMed, Web of Science, Scopus, and Cochrane Library before June 2020, two researchers independently selected nine randomized controlled trials (RCTs) in which any type of exercise was compared with no active treatment. We assessed the risk of bias in the included studies using the Cochrane risk-of-bias 2.0 tool for RCTs and computed the converged standardized mean difference with a 95% confidence interval. We found evidences for the positive effects of exercise on physical and psychological QoL scores in women with menopausal symptoms. However, there was no evidence for the effects of exercise on general, social, and menopause-specific QoL scores. The most common interventions for women with menopausal and urinary symptoms were yoga and pelvic floor muscle training (PFMT), respectively. In our meta-analyses, while yoga significantly improved physical QoL, its effects on general, psychological, sexual, and vasomotor symptoms QoL scores as well as the effect of PFMT on general QoL were not significant. Our findings suggest that well-designed studies are needed to confirm the effect of exercise on QoL in women with menopausal symptoms.

Office workers are at high risk for many chronic diseases, lowering their health-related quality of life (HRQOL). This systematic review and meta-analysis aimed to summarize the effects of physical exercise on HRQOL in office workers with and without health problems using data obtained from randomized controlled trials (RCTs), quasi-experimental, and observational studies. We searched PubMed, Web of Science, Scopus, Cochrane Library, and several grey literature databases, and identified 26 relevant studies for the synthesis. Overall, physical exercise significantly improved general (standardized mean difference (SMD) = 1.05; 95% confidence interval (CI): 0.66 to 1.44) and mental (SMD = 0.42; 95% CI: 0.19 to 0.66) HRQOL in office workers. Compared with healthy office workers, unhealthy office workers experienced greater improvements in general (unhealthy, SMD = 2.76; 95% CI: 1.63 to 3.89; healthy, SMD = 0.23; 95% CI: −0.09 to 0.56) and physical (unhealthy, SMD = 0.38; 95% CI: 0.17 to 0.58; healthy, SMD = −0.20; 95% CI: −0.51 to 0.11) HRQOL. Unsupervised physical exercise significantly improved general and mental HRQOL, while directly supervised physical exercise significantly improved only general HRQOL. Although physical exercise, especially unsupervised physical exercise, should be encouraged to improve HRQOL in office workers, detailed recommendations could not be made because of the diverse exercise types with different intensities. Therefore, further studies are needed to determine the optimal exercise for office workers with different health conditions.

Infectious respiratory diseases are highly contagious and very common, and thus can be considered as one of the leading causes of morbidity and mortality worldwide. We followed up the incidence rates (IRs) of eight infectious respiratory diseases, including chickenpox, measles, pertussis, mumps, invasive pneumococcal disease, scarlet fever, rubella, and meningococcal disease, after COVID-19 mitigation measures were implemented in South Korea, and then compared those with the IRs in the corresponding periods in the previous 3 years. Overall, the IRs of these diseases before and after age- or sex-standardization significantly decreased in the intervention period compared with the pre-intervention periods (p < 0.05 for all eight diseases). However, the difference in the IRs of all eight diseases between the IRs before and after age-standardization was significant (p < 0.05 for all periods), while it was not significant with regard to sex-standardization. The incidence rate ratios for eight diseases in the pre-intervention period compared with the intervention period ranged from 3.1 to 4.1. These results showed the positive effects of the mitigation measures on preventing the development of respiratory infectious diseases, regardless of age or sex, but we need to consider the age-structure of the population to calculate the effect size. In the future, some of these measures could be applied nationwide to prevent the occurrence or to reduce the transmission during outbreaks of these infections. This study provides evidence for strengthening the infectious disease management policies in South Korea.

DNA methylation modification plays a vital role in the pathophysiology of high blood pressure (BP). Herein, we applied three machine learning (ML) algorithms including deep learning (DL), support vector machine, and random forest for detecting high BP using DNA methylome data. Peripheral blood samples of 50 elderly individuals were collected three times at three visits for DNA methylome profiling. Participants who had a history of hypertension and/or current high BP measure were considered to have high BP. The whole dataset was randomly divided to conduct a nested five-group cross-validation for prediction performance. Data in each outer training set were independently normalized using a min–max scaler, reduced dimensionality using principal component analysis, then fed into three predictive algorithms. Of the three ML algorithms, DL achieved the best performance (AUPRC = 0.65, AUROC = 0.73, accuracy = 0.69, and F1-score = 0.73). To confirm the reliability of using DNA methylome as a biomarker for high BP, we constructed mixed-effects models and found that 61,694 methylation sites located in 15,523 intragenic regions and 16,754 intergenic regions were significantly associated with BP measures. Our proposed models pioneered the methodology of applying ML and DNA methylome data for early detection of high BP in clinical practices.

Deep learning (DL) is a distinct class of machine learning that has achieved first-class performance in many fields of study. For epigenomics, the application of DL to assist physicians and scientists in human disease-relevant prediction tasks has been relatively unexplored until very recently. In this article, we critically review published studies that employed DL models to predict disease detection, subtype classification, and treatment responses, using epigenomic data. A comprehensive search on PubMed, Scopus, Web of Science, Google Scholar, and arXiv.org was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Among 1140 initially identified publications, we included 22 articles in our review. DNA methylation and RNA-sequencing data are most frequently used to train the predictive models. The reviewed models achieved a high accuracy ranged from 88.3% to 100.0% for disease detection tasks, from 69.5% to 97.8% for subtype classification tasks, and from 80.0% to 93.0% for treatment response prediction tasks. We generated a workflow to develop a predictive model that encompasses all steps from first defining human disease-related tasks to finally evaluating model performance. DL holds promise for transforming epigenomic big data into valuable knowledge that will enhance the development of translational epigenomics.