Background: DNA methylation is one of the most phylogenetically widespread epigenetic modifications of genomic DNA. In particular, DNA methylation of transcription units (‘gene bodies’) is highly conserved across diverse taxa. However, the functional role of gene body methylation is not yet fully understood. A long-standing hypothesis posits that gene body methylation reduces transcriptional noise associated with spurious transcription of genes. Despite the plausibility of this hypothesis, an explicit test of this hypothesis has not been performed until now. Results: Using nucleotide-resolution data on genomic DNA methylation and abundant microarray data, here we investigate the relationship between DNA methylation and transcriptional noise. Transcriptional noise measured from microarrays scales down with expression abundance, confirming findings from single-cell studies. We show that gene body methylation is significantly negatively associated with transcriptional noise when examined in the context of other biological factors. Conclusions: This finding supports the hypothesis that gene body methylation suppresses transcriptional noise. Heavy methylation of vertebrate genomes may have evolved as a global regulatory mechanism to control for transcriptional noise. In contrast, promoter methylation exhibits positive correlations with the level of transcriptional noise. We hypothesize that methylated promoters tend to undergo more frequent transcriptional bursts than those that avoid DNA methylation.

DNA methylation is a critical regulatory mechanism implicated in development, learning, memory, and disease in the human brain. Here we have elucidated DNA methylation changes during recent human brain evolution. We demonstrate dynamic evolutionary trajectories of DNA methylation in cell-type and cytosine-context specific manner. Specifically, DNA methylation in non-CG context, namely CH methylation, has increased (hypermethylation) in neuronal gene bodies during human brain evolution, contributing to human-specific down-regulation of genes and co-expression modules. The effects of CH hypermethylation is particularly pronounced in early development and neuronal subtypes. In contrast, DNA methylation in CG context shows pronounced reduction (hypomethylation) in human brains, notably in cis-regulatory regions, leading to upregulation of downstream genes. We show that the majority of differential CG methylation between neurons and oligodendrocytes originated before the divergence of hominoids and catarrhine monkeys, and harbors strong signal for genetic risk for schizophrenia. Remarkably, a substantial portion of differential CG methylation between neurons and oligodendrocytes emerged in the human lineage since the divergence from the chimpanzee lineage and carries significant genetic risk for schizophrenia. Therefore, recent epigenetic evolution of human cortex has shaped the cellular regulatory landscape and contributed to the increased vulnerability to neuropsychiatric diseases.

Background: The Likert scale is an ordinal variable that measures the intensity of responses from research participants. It is widely used not only in social sciences, such as sociology and psychology, but also in survey-based research fields, such as nursing and public health. Among the approaches for handling the Likert-scale data, treating it as a continuous variable has been commonly used because it facilitates the application of parametric statistical methods and interpretation of results. In addition, from a perspective of statistical principle, this type of approach has been widely discussed and considered unproblematic. However, studies exploring the characteristics of the Likert scale in the approach with simulations are relatively rare. Thus, this study aimed to confirm the validity of the approach with simulation that compared the statistical characteristics of the Likert scale variable with those of variables from an assumed continuous latent distribution. Methods: In the Monte Carlo simulation study, the rectified normal distribution was specifically assumed for the continuous latent distribution. Then, type 1 error, statistical power, and correlation were compared across various situations involving both single and multiple variables. Results: From the simulation study, we found that a Likert scale with five or more points exhibited statistical characteristics comparable to those of the variable derived from the latent continuous distribution. Conclusions: Based on the results, we confirmed that the Likert scale can be used as a continuous variable in various parametric statistical methods. The corresponding suggested guidelines are expected to assist researchers in research design, data analysis, and results interpretation.

Shift work nurses experience multiple health problems due to irregular shifts and heavy job demands. However, the comorbidity patterns of nurses’ health problems and the association between health problems and turnover intention have rarely been studied. This study aimed to identify and cluster shift work nurses’ health problems and to reveal the associations between health problems and turnover intention. In this cross-sectional study, we analyzed data from 500 nurses who worked at two tertiary hospitals in Seoul, South Korea. Data, including turnover intention and nine types of health issues, were collected between March 2018 and April 2019. Hierarchical clustering and multiple ordinal logistic regressions were used for the data analysis. Among the participants, 22.2% expressed turnover intention and the mean number of health problems was 4.5 (range 0–9). Using multiple ordinal logistic regressions analysis, it was shown that sleep disturbance, depression, fatigue, a gastrointestinal disorder, and leg or foot discomfort as a single health problem significantly increased turnover intention. After clustering the health problems, four clusters were identified and only the neuropsychological cluster—sleep disturbance, fatigue, and depression—significantly increased turnover intention. We propose that health problems within the neuropsychological cluster must receive close attention and be addressed simultaneously to decrease nurse’s turnover intentions.

Older adults living alone face physical, emotional, and social health problems, and prefer to age in place (AIP) in their homes. A community-based integrated model for AIP is needed and few studies have identified its impact on older adults living alone. This was a non-randomized prospective study. Participants were 877 community-dwelling older adults living alone, aged above 65 years, in S* city in South Korea. The intervention group (n = 331) received a community-based integrated service (CBIS) model based on AIP for six months from October 2019 to April 2020. Scores on frailty (β = -0.377, p < .001), loneliness (β = -1.897, p = .018), and health-related quality of life (β = 4.299, p = .021) significantly improved in the intervention group. Among the intervention group, loneliness scores significantly improved among participants aged under 80 years than those aged over 80 years. The CBIS model improved frailty, loneliness, and quality of life in community-dwelling older adults living alone.

Purpose: To develop and validate a protein-based, multi-marker panel that provides superior pancreatic ductal adenocarcinoma (PDAC) detection abilities with sufficient diagnostic performance. Experimental Design: A total of 959 plasma samples from patients at multiple medical centers were used. To construct an optimal, diagnostic, multi-marker panel, we applied data preprocessing procedure to biomarker candidates. The multi-marker panel was developed using a training set comprised of 261 PDAC cases and 290 controls. Subsequent evaluations were performed in a validation set comprised of 65 PDAC cases and 72 controls. Further validation was performed in an independent set comprised of 75 PDAC cases and 47 controls. Results: A multi-marker panel containing 14 proteins was developed. The multi-marker panel achieved AUCs of 0.977 and 0.953 for the training set and validation set, respectively. In an independent validation set, the multi-marker panel yielded an AUC of 0.928. The diagnostic performance of the multi-marker panel showed significant improvements compared with carbohydrate antigen (CA) 19-9 alone (training set AUC = 0.977 vs. 0.872, P < 0.001; validation set AUC = 0.953 vs. 0.832, P < 0.01; independent validation set AUC = 0.928 vs. 0.771, P < 0.001). When the multi-marker panel and CA 19-9 were combined, the diagnostic performance of the combined panel was improved for all sets. Conclusions: This multi-marker panel and the combined panel showed statistically significant improvements in diagnostic performance compared with CA 19-9 alone and has the potential to complement CA 19-9 as a diagnostic marker in clinical practice.