Single-cell omics technologies have transformed our investigation of genomic, transcriptomic, and proteomic landscapes at the individual cell level. In particular, the application of single-cell RNA sequencing has unveiled the complex transcriptional variations inherent in cardiac cells, offering valuable perspectives into their dynamics. This review focuses on the integration of single-cell omics with induced pluripotent stem cells (iPSCs) in the context of cardiovascular research, offering a unique avenue to deepen our understanding of cardiac biology. By synthesizing insights from various single-cell technologies, we aim to elucidate the molecular intricacies of heart health and diseases. Beyond current methodologies, we explore the potential of emerging paradigms such as single-cell/spatial omics, delving into their capacity to reveal the spatial organization of cellular components within cardiac tissues. Furthermore, we anticipate their transformative role in shaping the future of cardiovascular research. This review aims to contribute to the advancement of knowledge in the field, offering a comprehensive perspective on the synergistic potential of transcriptomic analyses, iPSC applications, and the evolving frontier of spatial omics.

A fundamental objective of public health is to identify the causes of diseases and associated risk factors to develop effective prevention strategies. In this regard, the population attributable fraction (PAF) has become a key epidemiological measure for quantifying the proportion of disease incidence in a population attributable to specific risk factors.Current Concepts: The concept of PAF is widely applied in epidemiological and public health research, playing a crucial role in prioritizing disease prevention and management strategies. Estimating the PAF of cancer risk factors based on national data provides essential evidence for the formulation of government-led cancer control policies and prevention strategies. In particular, these estimates serve as critical indicators for evaluating cancer control programs and informing policy decisions. Given the variations in risk factor prevalence across different populations, it is crucial to estimate PAF using country-specific data to ensure the development of tailored and effective public health interventions.Discussion and Conclusion: This study underscores the importance of PAF as a foundational tool for evidencebased policymaking and highlights the need for periodic reassessment to enhance the effectiveness of cancer prevention and control efforts.

Single-cell omics technologies have transformed our investigation of genomic, transcriptomic, and proteomic landscapes at the individual cell level. In particular, the application of single-cell RNA sequencing has unveiled the complex transcriptional variations inherent in cardiac cells, offering valuable perspectives into their dynamics. This review focuses on the integration of single-cell omics with induced pluripotent stem cells (iPSCs) in the context of cardiovascular research, offering a unique avenue to deepen our understanding of cardiac biology. By synthesizing insights from various single-cell technologies, we aim to elucidate the molecular intricacies of heart health and diseases. Beyond current methodologies, we explore the potential of emerging paradigms such as single-cell/spatial omics, delving into their capacity to reveal the spatial organization of cellular components within cardiac tissues. Furthermore, we anticipate their transformative role in shaping the future of cardiovascular research. This review aims to contribute to the advancement of knowledge in the field, offering a comprehensive perspective on the synergistic potential of transcriptomic analyses, iPSC applications, and the evolving frontier of spatial omics.

A fundamental objective of public health is to identify the causes of diseases and associated risk factors to develop effective prevention strategies. In this regard, the population attributable fraction (PAF) has become a key epidemiological measure for quantifying the proportion of disease incidence in a population attributable to specific risk factors.Current Concepts: The concept of PAF is widely applied in epidemiological and public health research, playing a crucial role in prioritizing disease prevention and management strategies. Estimating the PAF of cancer risk factors based on national data provides essential evidence for the formulation of government-led cancer control policies and prevention strategies. In particular, these estimates serve as critical indicators for evaluating cancer control programs and informing policy decisions. Given the variations in risk factor prevalence across different populations, it is crucial to estimate PAF using country-specific data to ensure the development of tailored and effective public health interventions.Discussion and Conclusion: This study underscores the importance of PAF as a foundational tool for evidencebased policymaking and highlights the need for periodic reassessment to enhance the effectiveness of cancer prevention and control efforts.

A fundamental objective of public health is to identify the causes of diseases and associated risk factors to develop effective prevention strategies. In this regard, the population attributable fraction (PAF) has become a key epidemiological measure for quantifying the proportion of disease incidence in a population attributable to specific risk factors.Current Concepts: The concept of PAF is widely applied in epidemiological and public health research, playing a crucial role in prioritizing disease prevention and management strategies. Estimating the PAF of cancer risk factors based on national data provides essential evidence for the formulation of government-led cancer control policies and prevention strategies. In particular, these estimates serve as critical indicators for evaluating cancer control programs and informing policy decisions. Given the variations in risk factor prevalence across different populations, it is crucial to estimate PAF using country-specific data to ensure the development of tailored and effective public health interventions.Discussion and Conclusion: This study underscores the importance of PAF as a foundational tool for evidencebased policymaking and highlights the need for periodic reassessment to enhance the effectiveness of cancer prevention and control efforts.

Single-cell omics technologies have transformed our investigation of genomic, transcriptomic, and proteomic landscapes at the individual cell level. In particular, the application of single-cell RNA sequencing has unveiled the complex transcriptional variations inherent in cardiac cells, offering valuable perspectives into their dynamics. This review focuses on the integration of single-cell omics with induced pluripotent stem cells (iPSCs) in the context of cardiovascular research, offering a unique avenue to deepen our understanding of cardiac biology. By synthesizing insights from various single-cell technologies, we aim to elucidate the molecular intricacies of heart health and diseases. Beyond current methodologies, we explore the potential of emerging paradigms such as single-cell/spatial omics, delving into their capacity to reveal the spatial organization of cellular components within cardiac tissues. Furthermore, we anticipate their transformative role in shaping the future of cardiovascular research. This review aims to contribute to the advancement of knowledge in the field, offering a comprehensive perspective on the synergistic potential of transcriptomic analyses, iPSC applications, and the evolving frontier of spatial omics.

Objective: The coronavirus disease-2019 (COVID-19) pandemic’s social isolation has significantly impacted mental health, increasing depression and anxiety. This study explores the effects of social isolation on both humans and mice, focusing on behavioral changes and hippocampal protein expression. It also investigates genetic alterations through single-cell RNA and whole-genome sequencing (WGS). Methods: Here we conducted behavioral studies, protein expression studies, single-nucleus sequencing (snRNAseq), and WGS of the hippocampus of mice that underwent early maternal separation and social isolation, and a demographic study of community populations who had been self-quarantined owing to COVID-19 exposure to investigate the link between somatic mutations and stress due to social isolation. Results: The demographic study demonstrated more negative mental health findings among individuals who live alone or are single. Mice subjected to early maternal separation and social isolation demonstrated increased anxiety-like behaviors and stress-related corticotropin-releasing hormone receptor 1, and neurogenesis-related sex-determining region Y-box 2 and doublecortin expression. In snRNA-seq, differences, such as transthyretin increase, were observed in the maternal separation group, and somatic mutations, including insertion in the intron site of Tmem267, were observed in the social isolation group on WGS. Conclusion The results of this study suggest that stress, such as social isolation, can cause changes at the genetic level, as well as behavioral and brain protein changes.

Hikikomori syndrome (HS), a phenomenon characterized by social withdrawal and isolation, has attracted significant attention in both academic and clinical settings. However, understanding the diverse nature of HS remains a challenge due to its multifaceted etiology and presentation. This paper aims to shed light on this phenomenon by examining three representative typologies of HS in clinical settings. Through detailed case analyses, we categorize HS into three main types: HS associated with neurodevelopmental disorders; HS triggered by the onset of mental illness; and HS emerging gradually with age-related challenges. By elucidating these typologies, we provide insights into the complex interplay of psychological, social, and developmental factors contributing to HS, thereby facilitating more tailored approaches for the evaluation of and intervention into this syndrome in clinical practice.

Objective: This study aimed to investigate the association between gaming time and problematic game use (PGU) within a large sample of Korean male gamers and to examine the potential moderating effects of loneliness, living alone, and household size. Methods: This study employed data from 743 male gamers from the National Mental Health Survey 2021, a nationally representative survey of mental illness conducted in South Korea. Self-reported data on the average gaming time per day, severity of PGU, loneliness, living alone, and household size were used. Results: Gaming time was positively associated with PGU and this relationship was significantly moderated by loneliness such that the positive effect of gaming time on PGU was greater when the levels of loneliness were high. The three-way interaction effect of gaming time, loneliness, and living alone was also significant, in that the moderating effect of loneliness on the relationship between gaming time and PGU was significant only in the living alone group. However, household size (i.e., number of housemates) did not moderate the interaction between gaming time and loneliness among gamers living with housemates. Conclusion These results suggest the importance of considering loneliness and living arrangements of male gamers, in addition to gaming time, in identifying and intervening with individuals at heightened risk of PGU.

During the coronavirus disease 2019 (COVID-19) pandemic, conclusively evaluating possible associations between COVID-19 vaccines and potential adverse events was of critical importance. The National Academy of Medicine of Korea established the COVID-19 Vaccine Safety Research Center (CoVaSC) with support from the Korea Disease Control and Prevention Agency to investigate the scientific relationship between COVID-19 vaccines and suspected adverse events. Although determining whether the COVID-19 vaccine was responsible for any suspected adverse event necessitated a systematic approach, traditional causal inference theories, such as Hill's criteria, encountered certain limitations and criticisms. To facilitate a systematic and evidence-based evaluation, the United States Institute of Medicine, at the request of the Centers for Disease Control and Prevention, offered a detailed causality assessment framework in 2012, which was updated in the recent report by the National Academies of Sciences, Engineering, and Medicine (NASEM) in 2024.This framework, based on a weight-of-evidence approach, allows the independent evaluation of both epidemiological and mechanistic evidence, culminating in a comprehensive conclusion about causality. Epidemiological evidence derived from population studies is categorized into four levels—high, moderate, limited, or insufficient—while mechanistic evidence, primarily from biological and clinical studies in animals and individuals, is classified as strong, intermediate, weak, or lacking. The committee then synthesizes these two types of evidence to draw a conclusion about the causal relationship, which can be described as “convincingly supports” (“evidence established” in the 2024 NASEM report), “favors acceptance,” “favors rejection,” or “inadequate to accept or reject.” The CoVaSC has established an independent committee to conduct causality assessments using the weightof-evidence framework, specifically for evaluating the causality of adverse events associated with COVID-19 vaccines. The aim of this study is to provide an overview of the weight-ofevidence framework and to detail the considerations involved in its practical application in the CoVaSC.