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.

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.

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.

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.

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.

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.

Background: Conventional modality requires several days observation by Holter monitor to differentiate atrial fibril‑ lation (AF) between Paroxysmal atrial fibrillation (PAF) and Non-paroxysmal atrial fibrillation (Non-PAF). Rapid and practical differentiating approach is needed. Objective: To develop a machine learning model that observes 10-s of standard 12-lead electrocardiograph (ECG) for real-time classification of AF between PAF versus Non-PAF. Methods: In this multicenter, retrospective cohort study, the model training and cross-validation was performed on a dataset consisting of 741 patients enrolled from Severance Hospital, South Korea. For cross-institutional validation, the trained model was applied to an independent data set of 600 patients enrolled from Ewha University Hospital, South Korea. Lasso regression was applied to develop the model. Results: In the primary analysis, the Area Under the Receiver Operating Characteristic Curve (AUC) on the test set for the model that predicted AF subtype only using ECG was 0.72 (95% CI 0.65–0.80). In the secondary analysis, AUC only using baseline characteristics was 0.53 (95% CI 0.45–0.61), while the model that employed both baseline characteris‑ tics and ECG parameters was 0.72 (95% CI 0.65–0.80). Moreover, the model that incorporated baseline characteristics, ECG, and Echocardiographic parameters achieved an AUC of 0.76 (95% CI 0.678–0.855) on the test set. Conclusions Our machine learning model using ECG has potential for automatic differentiation of AF between PAF versus Non-PAF achieving high accuracy. The inclusion of  Echocardiographic parameters further increases model per‑ formance. Further studies are needed to clarify the next steps towards clinical translation of the proposed algorithm.

Background: Acinetobacter baumannii infections cause high morbidity and mortality in intensive care unit (ICU) patients. However, there are limited data on the changes of longterm epidemiology of imipenem resistance in A. baumannii bacteremia among pediatric ICU (PICU) patients. Methods: A retrospective review was performed on patients with A. baumannii bacteremia in PICU of a tertiary teaching hospital from 2000 to 2016. Antimicrobial susceptibility tests, multilocus sequence typing (MLST), and polymerase chain reaction for antimicrobial resistance genes were performed for available isolates. Results: A. baumannii bacteremia occurred in 27 patients; imipenem-sensitive A. baumannii (ISAB, n = 10, 37%) and imipenem-resistant A. baumannii (IRAB, n = 17, 63%). There was a clear shift in the antibiogram of A. baumannii during the study period. From 2000 to 2003, all isolates were ISAB (n = 6). From 2005 to 2008, both IRAB (n = 5) and ISAB (n = 4) were isolated. However, from 2009, all isolates were IRAB (n = 12). Ten isolates were available for additional test and confirmed as IRAB. MLST analysis showed that among 10 isolates, sequence type 138 was predominant (n = 7). All 10 isolates were positive for OXA-23-like and OXA-51-like carbapenemase. Of 27 bacteremia patients, 11 were male (41%), the median age at bacteremia onset was 5.2 years (range, 0–18.6 years). In 33% (9/27) of patients, A. baumannii was isolated from tracheal aspirate prior to development of bacteremia (median, 8 days; range, 5–124 days). The overall case-fatality rate was 63% (17/27) within 28 days. There was no statistical difference in the case fatality rate between ISAB and IRAB groups (50% vs. 71%; P = 0.422). Conclusion IRAB bacteremia causes serious threat in patients in PICU. Proactive infection control measures and antimicrobial stewardship are crucial for managing IRAB infection in PICU.

Objective: With the recent development of various MRI-conditional cardiac implantable electronic devices (CIEDs), the accurate identification and characterization of CIEDs have become critical when performing MRI in patients with CIEDs. We aimed to develop and evaluate a deep learning-based algorithm (DLA) that performs the detection and characterization of parameters, including MRI safety, of CIEDs on chest radiograph (CR) in a single step and compare its performance with other related algorithms that were recently developed. Materials and Methods: We developed a DLA (X-ray CIED identification [XCID]) using 9912 CRs of 958 patients with 968 CIEDs comprising 26 model groups from 4 manufacturers obtained between 2014 and 2019 from one hospital. The performance of XCID was tested with an external dataset consisting of 2122 CRs obtained from a different hospital and compared with the performance of two other related algorithms recently reported, including PacemakerID (PID) and Pacemaker identification with neural networks (PPMnn). Results: The overall accuracies of XCID for the manufacturer classification, model group identification, and MRI safety characterization using the internal test dataset were 99.7% (992/995), 97.2% (967/995), and 98.9% (984/995), respectively. These were 95.8% (2033/2122), 85.4% (1813/2122), and 92.2% (1956/2122), respectively, with the external test dataset. In the comparative study, the accuracy for the manufacturer classification was 95.0% (152/160) for XCID and 91.3% for PPMnn (146/160), which was significantly higher than that for PID (80.0%,128/160; p < 0.001 for both). XCID demonstrated a higher accuracy (88.1%; 141/160) than PPMnn (80.0%; 128/160) in identifying model groups (p < 0.001). Conclusion The remarkable and consistent performance of XCID suggests its applicability for detection, manufacturer and model identification, as well as MRI safety characterization of CIED on CRs. Further studies are warranted to guarantee the safe use of XCID in clinical practice.

Purpose: : This study aimed to comprehensively understand the work experience of the person in charge of the adequacy evaluation of small-and medium-sized hospitals and explore its meaning and essence in-depth. Methods: : This was a descriptive qualitative study. The study participants were 10 nurses who understood the purpose of this study and participated voluntarily. Data collection was conducted via in-depth interviews in January 2021. The interviews were conducted 1-2 times per participant and lasted approximately 40-50 minutes per session. Data analysis was performed using a qualitative content analysis. Results: : The work experience of the person in charge of the adequacy evaluation of small-and medium-sized hospitals included four themes: “difficulty in preparing for evaluation,” “negative views on evaluation,” “lack of a support system,” and “positive improvements and changes due to an evaluation.” Conclusion : Based on the above results, an education program and support system should be developed to strengthen the competence of nurses in charge of the adequacy evaluation of small- and medium-sized hospitals.