Factors Influencing the Acceptance of Distributed Research Networks in Korea: Data Accessibility and Data Security Risk / 대한의료정보학회지

Objectives@#Distributed research networks (DRNs) facilitate multicenter research by enabling the use of multicenter data; therefore, they are increasingly utilized in healthcare fields. Despite the numerous advantages of DRNs, it is crucial to understand researchers' acceptance of these networks to ensure their effective application in multicenter research. In this study, we sought to identify the factors influencing the adoption of DRNs among researchers in Korea. @*Methods@#We used snowball sampling to collect data from 149 researchers between July 7 and August 28, 2020. Five factors were used to formulate the hypotheses and research model: data accessibility, usefulness, ease of use, data security risk, and intention to use DRNs. We applied a structural equation model to identify relationships within the research model. @*Results@#Data accessibility and data security were critical to the acceptance and use of DRNs. The usefulness of DRNs partially mediated the relationship between data accessibility and the intention to use DRNs. Interestingly, ease of use did not influence the intention to use DRNs, but it was affected by data accessibility. Furthermore, ease of use impacted the perceived usefulness of DRNs. @*Conclusions@#This study highlighted major factors that can promote the broader adoption and utilization of DRNs. Consequently, these findings can contribute to the expansion of active multicenter research using DRNs in the field of healthcare research.

Secondary migration of a pre-existing central venous catheter due to a Swan-Ganz catheter insertion – A case report –

The entanglement of multiple central venous catheters is a rare and seriouscomplication. The Swan-Ganz catheter is a responsible for various cases.Case: A 66-year-old male patient was under general anesthesia for a coronary artery bypassgraft surgery. As he had a pre-existing Perm catheter in the right subclavian vein, a SwanGanz catheter was inserted into the left internal jugular vein. Chest radiograph after catheterplacement revealed that the Perm catheter had migrated to the left brachiocephalic vein.The surgeon attempted to reposition it manually, but postoperative radiograph showed thatit had rolled into a loop. On postoperative day 1, radiological intervention was performed tountangle the loop, which was successful.Conclusions: After placing a Swan-Ganz catheter in patients with a pre-existing central venous catheter, the presence of entanglement should be assessed. In such cases, radiology-guided correction is recommended, as a blind attempt to disentangle can aggravate thecondition.

Predictors of Habitual and Addictive Smartphone Behavior in Problematic Smartphone Use

Objective@#Smartphones have become common, and problematic smartphone use (PSU) is increasing. Predictors of PSU should be identified to prevent it. Little is known about the role of content types of smartphone use as predictors of PSU. Therefore, we aimed to evaluate the predictors of two proposed concepts of PSU, namely habitual smartphone behavior (SB) and addictive SB, within the context of the application (app) categories. @*Methods@#We studied 1,039 smartphone users using online surveys conducted between January 2 and 31, 2019. We employed multiple regression analysis to identify the predictors of habitual and addictive SB. We controlled for sex and age (mean=39.20). @*Results@#Common predictors of habitual and addictive SB were the use of social networking services, games, entertainment apps, and average weekend smartphone usage time. The predictors of habitual SB were the use of web and lifestyle apps, weekly usage frequency, and sex (female) and the predictors of addictive SB were the use of shopping apps and sleep duration. @*Conclusion@#This study revealed the need to consider habitual and addictive SB in evaluating PSU. The predictors in terms of the content types of smartphone usage can be used to develop monitoring and prevention services for PSU.

Single-cell transcriptomics: a novel precision medicine technique in nephrology

Due to the complex structure and function of the kidneys, the mechanism of kidney disease is unclear. In particular, transcriptomics approaches at the bulk level are unable to differentiate primary autonomous responses, which lead to disease development, from secondary cell non-autonomous responses. Single-cell analysis techniques can overcome the limitations inherent in the measurement of heterogeneous cell populations and clarify the central issues in kidney biology and disease pathogenesis. Single-cell sequencing helps in identifying disease-related biomarkers and pathways, stratifying patients, and deciding on appropriate treatment methods. Here we review a variety of single-cell analysis techniques and single-cell transcriptomics studies performed in the field of nephrology. Moreover, we discuss the future prospects of single-cell analysis-based precision medicine in nephrology.

Transcriptome Analysis Identifies Altered Biological Processes and Novel Markers in Human Immunodeficiency Virus-1 Long-Term Non-Progressors / 감염과화학요법

Background@#The latent reservoir of Human Immunodificiency Virus-1 (HIV-1) has been a major barrier to the complete eradication of HIV-1 and the development of HIV therapy. Longterm non-progressors (LTNPs) are a rare group of patients with HIV-1 who can spontaneously control HIV-1 replication without antiretroviral therapy. Transcriptome analysis is necessary to predict the pathways involved in the natural control of HIV-1, elucidate the mechanisms involved in LTNPs, and find biomarkers for HIV-1 reservoir therapy. @*Materials and Methods@#In this study, we obtained peripheral blood mononuclear cells from two LTNP subjects at multiple time points and performed RNA-sequencing analyses. @*Results@#We found that LTNPs and normal subjects had different transcriptome profiles. Functional annotation analysis identified that differentially expressed genes in LTNPs were enriched in several biological pathways such as cell cycle-related pathways and the transforming growth factor-beta signaling pathway. However, genes that were downregulated in LTNPs were associated with immune responses such as the interferon response and IL2-STAT5 signaling. Protein-protein interaction network analysis showed that CD8A, KLRD1, ASGR1, and MLKL, whose gene expression was upregulated in LTNPs, directly interacted with HIV-1 proteins. The network analysis also found that viral proteins potentially regulated host genes that were associated with immune system processes, metabolic processes, and gene expression regulation. @*Conclusion@#Our longitudinal transcriptome analysis of the LTNPs identified multiple previously undescribed pathways and genes that may be useful in the discovery of novel therapeutic targets and biomarkers.

Single-cell transcriptomics: a novel precision medicine technique in nephrology

Due to the complex structure and function of the kidneys, the mechanism of kidney disease is unclear. In particular, transcriptomics approaches at the bulk level are unable to differentiate primary autonomous responses, which lead to disease development, from secondary cell non-autonomous responses. Single-cell analysis techniques can overcome the limitations inherent in the measurement of heterogeneous cell populations and clarify the central issues in kidney biology and disease pathogenesis. Single-cell sequencing helps in identifying disease-related biomarkers and pathways, stratifying patients, and deciding on appropriate treatment methods. Here we review a variety of single-cell analysis techniques and single-cell transcriptomics studies performed in the field of nephrology. Moreover, we discuss the future prospects of single-cell analysis-based precision medicine in nephrology.

Transcriptome Analysis Identifies Altered Biological Processes and Novel Markers in Human Immunodeficiency Virus-1 Long-Term Non-Progressors / 감염과화학요법

Background@#The latent reservoir of Human Immunodificiency Virus-1 (HIV-1) has been a major barrier to the complete eradication of HIV-1 and the development of HIV therapy. Longterm non-progressors (LTNPs) are a rare group of patients with HIV-1 who can spontaneously control HIV-1 replication without antiretroviral therapy. Transcriptome analysis is necessary to predict the pathways involved in the natural control of HIV-1, elucidate the mechanisms involved in LTNPs, and find biomarkers for HIV-1 reservoir therapy. @*Materials and Methods@#In this study, we obtained peripheral blood mononuclear cells from two LTNP subjects at multiple time points and performed RNA-sequencing analyses. @*Results@#We found that LTNPs and normal subjects had different transcriptome profiles. Functional annotation analysis identified that differentially expressed genes in LTNPs were enriched in several biological pathways such as cell cycle-related pathways and the transforming growth factor-beta signaling pathway. However, genes that were downregulated in LTNPs were associated with immune responses such as the interferon response and IL2-STAT5 signaling. Protein-protein interaction network analysis showed that CD8A, KLRD1, ASGR1, and MLKL, whose gene expression was upregulated in LTNPs, directly interacted with HIV-1 proteins. The network analysis also found that viral proteins potentially regulated host genes that were associated with immune system processes, metabolic processes, and gene expression regulation. @*Conclusion@#Our longitudinal transcriptome analysis of the LTNPs identified multiple previously undescribed pathways and genes that may be useful in the discovery of novel therapeutic targets and biomarkers.

Molecular Mimicry Map (3M) of SARS-CoV-2: Prediction of potentially immunopathogenic SARS-CoV-2 epitopes via a novel immunoinformatic approach

Currently, more than 33 million peoples have been infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and more than a million people died from coronavirus disease 2019 (COVID-19), a disease caused by the virus. There have been multiple reports of autoimmune and inflammatory diseases following SARS-CoV-2 infections. There are several suggested mechanisms involved in the development of autoimmune diseases, including cross-reactivity (molecular mimicry). A typical workflow for discovering cross-reactive epitopes (mimotopes) starts with a sequence similarity search between protein sequences of human and a pathogen. However, sequence similarity information alone is not enough to predict cross-reactivity between proteins since proteins can share highly similar conformational epitopes whose amino acid residues are situated far apart in the linear protein sequences. Therefore, we used a hidden Markov model-based tool to identify distant viral homologs of human proteins. Also, we utilized experimentally determined and modeled protein structures of SARS-CoV-2 and human proteins to find homologous protein structures between them. Next, we predicted binding affinity (IC50) of potentially cross-reactive T-cell epitopes to 34 MHC allelic variants that have been associated with autoimmune diseases using multiple prediction algorithms. Overall, from 8,138 SARS-CoV-2 genomes, we identified 3,238 potentially cross-reactive B-cell epitopes covering six human proteins and 1,224 potentially cross-reactive T-cell epitopes covering 285 human proteins. To visualize the predicted cross-reactive T-cell and B-cell epitopes, we developed a web-based application "Molecular Mimicry Map (3M) of SARS-CoV-2" (available at https://ahs2202.github.io/3M/). The web application enables researchers to explore potential cross-reactive SARS-CoV-2 epitopes alongside custom peptide vaccines, allowing researchers to identify potentially suboptimal peptide vaccine candidates or less ideal part of a whole virus vaccine to design a safer vaccine for people with genetic and environmental predispositions to autoimmune diseases. Together, the computational resources and the interactive web application provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune disease following COVID-19.

Introduction to digital pathology and computer-aided pathology

Digital pathology (DP) is no longer an unfamiliar term for pathologists, but it is still difficult for many pathologists to understand the engineering and mathematics concepts involved in DP. Computer-aided pathology (CAP) aids pathologists in diagnosis. However, some consider CAP a threat to the existence of pathologists and are skeptical of its clinical utility. Implementation of DP is very burdensome for pathologists because technical factors, impact on workflow, and information technology infrastructure must be considered. In this paper, various terms related to DP and computer-aided pathologic diagnosis are defined, current applications of DP are discussed, and various issues related to implementation of DP are outlined. The development of computer-aided pathologic diagnostic tools and their limitations are also discussed.

A low-cost video-oculography system for vestibular function testing.

In order to remain in focus during head movements, vestibular-ocular reflex causes eyes to move in the opposite direction to head movement. Disorders of vestibular system decrease vision, causing abnormal nystagmus and dizziness. To diagnose abnormal nystagmus, various studies have been reported including the use of rotating chair tests and videonystagmography. However, these tests are unsuitable for home use due to their high costs. Thus, a low-cost video-oculography system is necessary to obtain clinical features at home. In this paper, we present a low-cost video-oculography system using an infrared camera and Raspberry Pi board for tracking the pupils and evaluating a vestibular system. Horizontal eye movement is derived from video data obtained from an infrared camera and infrared light-emitting diodes, and the velocity of head rotation is obtained from a gyroscope sensor. Each pupil was extracted using a morphology operation and a contour detection method. Rotatory chair tests were conducted with our developed device. To evaluate our system, gain, asymmetry, and phase were measured and compared with System 2000. The average IQR errors of gain, phase and asymmetry were 0.81, 2.74 and 17.35, respectively. We showed that our system is able to measure clinical features.