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.

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.

In the pathogenesis of inflammatory diseases such as allergies, S100A8 acts as an important molecule and T lymphocytes are essential cytokine-releasing cells. In this study, we investigated the effect of S100A8 on release of cytokines, specifically MCP-1, IL-6, and IL-8 in T cells, and its associated signaling mechanism. S100A8 increased secretion of MCP-1, IL-6, and IL-8 in a time-and dose-dependent manner. Elevated secretion of MCP-1, IL-6, and IL-8 due to S100A8 was inhibited by the TLR4 inhibitor TLR4i, the PI3K inhibitor LY294002, the PKCδ inhibitor rottlerin, the ERK inhibitor PD98059, the p38 MAPK inhibitor SB202190, the JNK inhibitor SP600125, and the NF-κB inhibitor BAY-11-7085. S100A8 induced phosphorylation of ERK, p38 MAPK, and JNK in a time-dependent manner, and activation was suppressed by TLR4i, LY294002, and rottlerin. S100A8 induced NF-κB activation by Iκ-Bα degradation, and NF-κB activity was suppressed by PD98059, SB202190, and SP600125. These results indicate that S100A8 induces cytokine release via TLR4. Study of PI3K, PKCδ, MAPKs, and NF-κB will contribute to elucidation of the S100A8invovled mechanism.

Childhood obesity could contribute to adulthood obesity, leading to adverse health outcomes in adults. However, the mechanisms for how obesity is developed are still unclear. To determine the epigenome-wide and genome-wide expression changes related with childhood obesity, we compared microRNome and transcriptome levels as well as leptin protein levels in whole bloods of 12 obese and 24 normal children aged 6 years. miR-328-3p, miR-1301-3p, miR-4685-3p, and miR-6803-3p were negatively associated with all obesity indicators. The four miRNAs were also associated with 3948 mRNAs, and separate 475 mRNAs (185 among 3948 mRNAs) were associated with all obesity indicators. The 2533 mRNAs (64.2%) among the 3948 mRNAs and 286 mRNAs (60.2%) among the 475 mRNAs were confirmed as targets of the four miRNAs in public databases through miRWalk 2.0. Leptin protein was associated with miR-6803-3p negatively and all obesity indicators positively. Using DAVID bioinformatics resources 6.8, top three pathways for obesity-related gene set were metabolic pathways, pathways in cancer, and PI3K-Akt signaling pathway. The top three obesity-related disease classes were metabolic, cardiovascular, and chemdependency. Our results support that childhood obesity could be developed through miRNAs-related epigenetic mechanism and, further, these obesity-related epigenetic changes could control the pathways related with the development of various diseases.

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