Trends of fear and anger on YouTube during the initial stage of the COVID-19 outbreak in South Korea.

BACKGROUND: The COVID-19 pandemic has been the most widespread and threatening health crisis experienced by the Korean society. Faced with an unprecedented threat to survival, society has been gripped by social fear and anger, questioning the culpability of this pandemic. This study explored the correlation between social cognitions and negative emotions and their changes in response to the severe events stemming from the COVID-19 pandemic in South Korea. METHODS: The analysis was based on a cognitive-emotional model that links fear and anger to the social causes that trigger them and used discursive content from comments posted on YouTube's COVID-19-related videos. A total of 182,915 comments from 1,200 videos were collected between January and December 2020. We performed data analyses and visualizations using R, Netminer 4.0, and Gephi software and calculated Pearson's correlation coefficients between emotions. RESULTS: YouTube videos were analyzed for keywords indicating cognitive assessments of major events related to COVID-19 and keywords indicating negative emotions. Eight topics were identified through topic modeling: causes and risks, perceptions of China, media and information, infection prevention rules, economic activity, school and infection, political leaders, and religion, politics, and infection. The correlation coefficient between fear and anger was 0.462 (p < .001), indicating a moderate linear relationship between the two emotions. Fear was the highest from January to March in the first year of the COVID-19 outbreak, while anger occurred before and after the outbreak, with fluctuations in both emotions during this period. CONCLUSIONS: This study confirmed that social cognitions and negative emotions are intertwined in response to major events related to the COVID-19 pandemic, with each emotion varying individually rather than being ambiguously mixed. These findings could aid in developing social cognition-emotion-based public health strategies through education and communication during future pandemic outbreaks.

The mediating effect of workplace incivility on organization culture in South Korea: A descriptive correlational analysis of the turnover intention of nurses.

PURPOSE: The purpose of this study was to understand the mediating effect of workplace incivility on the relationship between nursing organizational culture and turnover intention among nurses. DESIGN: A descriptive survey was used to collect data. The participants were 170 nurses with more than six months of clinical experience at university hospitals or hospitals with over 500 beds in South Korea. METHODS: Data were collected using self-report questionnaires. Collected data were analyzed using descriptive statistics, t-test, ANOVA, Scheffé test, and Pearson's correlation. Baron and Kenny's three-step hierarchical regression analysis and the Sobel test were used to determine the mediating effect of workplace incivility on the relationship between nursing organizational culture and nurses' turnover intention. RESULTS: This study found a full mediating effect of workplace incivility on the association between relationship-oriented culture and turnover intention (Z = -3.02, p = 0.003) and a partial mediating effect of workplace incivility on the association between hierarchy-oriented culture and turnover intention (Z = 2.36, p = 0.018). CONCLUSION: This study empirically confirmed that nursing organizational culture and workplace incivility directly or indirectly influenced turnover intention, which highlights the seriousness of workplace incivility. CLINICAL RELEVANCE: This study suggests that there is a need to establish a concrete strategy to avoid a hierarchy-oriented culture and create a relationship-oriented culture. It is important to develop a variety of intervention programs to reduce workplace incivility in order to prevent nurses' turnover.

Effects of job embeddedness and nursing working environment on turnover intention among trauma centre nurses: A cross-sectional study.

AIM: To investigate the effects of job embeddedness and nursing working environment on trauma centre nurses' turnover intention. BACKGROUND: Trauma centre nurses have higher average turnover intention than hospital nurses. However, factors that increase the turnover intention of trauma centre nurses remain unexplored. METHODS: This cross-sectional study was conducted from August to October 2019, with 120 trauma centre nurses working at three trauma centres in B, D and U cities using measures of demographic characteristics, job embeddedness, nursing working environment and turnover intention. RESULTS: The mean turnover intention score was 3.60/5 points. There were significant correlations among turnover intention and fit, sacrifice, foundation for quality nursing, ability and leadership of nursing managers, cooperation of nurses and doctors, nurse participation in hospital management and sufficient manpower and material support. Turnover intention was predicted by nurse participation in hospital management, gender, clinical experience and fit, which explained 54%. CONCLUSIONS: Factors that influence nurses' turnover intention at trauma centres were gender, clinical experience, job fit and, especially, nurses' participation in hospital management, which had the most effect on the nursing working environment. IMPLICATIONS FOR NURSING MANAGEMENT: To expand participation of trauma centre nurses, hospital management systems and organisational culture need improvement.

Text network analysis of research topics and trends on global health nursing literature from 1974 -2017.

AIMS: The purpose of this study is to examine the relationship between keywords in existing global health nursing studies during 44 years (1974-2017) and to develop schematic diagrams of the relationship between these keywords from a macro perspective. It is to identify the trend of the literature in global health nursing field. DESIGN: A descriptive bibliometric analysis of publications in global health nursing. METHODS: The keywords from 7,115 articles and literatures were examined using the Text Rank Analyzer via the applied text network analysis with NetMiner 4.0. RESULTS: As for global health nursing, keywords with the most frequent appearance and the highest networking degree in centrality were 'study', 'patient', 'nurse', and 'women'. Six central keywords were also found highly related to other keywords: 'global health nursing', 'study', 'patient', 'care', 'nurse', and 'education'. By measuring the degree of keywords connected to other keywords in centrality, six clusters were established. Then, emerging topics assessed by time periods were identified as follows: the beginning phase ('breastfeeding', 'women', and 'children'), the development phase ('quality', 'life', and 'human immunodeficiency virus'), the maturation phase ('mental health', 'depression', and 'global health'), and the expansion phase ('pregnancy', 'palliative care', and 'infectious disease'). CONCLUSION: The identified trends on this study will help nurse leaders to grasp the trends and insights for global health and to train future nurses to serve clients better in the practice fields. IMPACT: Keywords with the highest appearance and centrality in the network were found in the global health articles. The bibliometric analysis showed various subjects according to the following phases: beginning development maturation and expansion. The awareness of the trend change in the global health helps nursing researchers and educators modify the curriculum of global health nursing and train future nurses to be equipped with the global health competencies.

Effects of the e-Motivate4Change Program on Metabolic Syndrome in Young Adults Using Health Apps and Wearable Devices: Quasi-Experimental Study.

BACKGROUND: The health behaviors of young adults lag behind those of other age groups, and active health management is needed to improve health behaviors and prevent chronic diseases. In addition, developing good lifestyle habits earlier in life could reduce the risk of metabolic syndrome (MetS) later on. OBJECTIVE: The aim of this study is to investigate the effects of the e-Motivate4Change program, for which health apps and wearable devices were selected based on user needs. The program was developed for the prevention and management of MetS in young adults. METHODS: This experimental study used a nonequivalent control group. In total, 59 students from 2 universities in Daegu, Korea participated in the study (experimental group n=30; control group n=29). Data were collected over 4 months, from June 1 to September 30, 2018. The experimental group received a 12-week e-Motivate4Change program intervention, and the control group received MetS education and booklets without the e-Motivate4Change program intervention. RESULTS: After the program, the experimental group had significantly higher scores for health-related lifestyle (t=3.86; P<.001) and self-efficacy (t=6.00; P<.001) than did the control group. Concerning BMI, there were significant effects by group (F=1.01; P<.001) and for the group × time interaction (F=4.71; P=.034). Concerning cholesterol, there were significant main effects for group (F=4.32; P=.042) and time (F=9.73; P<.001). CONCLUSIONS: The e-Motivate4Change program effectively improved participants' health-related lifestyle scores and self-efficacy, and significantly reduced their BMI and cholesterol levels. The program can be used to identify and prevent MetS among young adults.

African Swine Fever Virus in Pork Brought into South Korea by Travelers from China, August 2018.

We tested samples of pork products confiscated from travelers to South Korea for African swine fever virus (ASFV). We detected ASFV in 4 food items confiscated from travelers from Shenyang, China, in August 2018. Surveillance of pork products at country entry points is needed to mitigate the risk for ASFV introduction.

Research topics and trends in medical education by social network analysis.

BACKGROUND: As studies analyzing the networks and relational structures of research topics in academic fields emerge, studies that apply methods of network and relationship analysis, such as social network analysis (SNA), are drawing more attention. The purpose of this study is to explore the interaction of medical education subjects in the framework of complex systems theory using SNA and to analyze the trends in medical education. METHODS: The authors extracted keywords using Medical Subject Headings terms from 9,379 research articles (162,866 keywords) published in 1963-2015 in PubMed. They generated an occurrence frequency matrix, calculated relatedness using Weighted Jaccard Similarity, and analyzed and visualized the networks with Gephi software. RESULTS: Newly emerging topics by period units were identified as historical trends, and 20 global-level topic clusters were obtained through network analysis. A time-series analysis led to the definition of five historical periods: the waking phase (1963-1975), the birth phase (1976-1990), the growth phase (1991-1996), the maturity phase (1997-2005), and the expansion phase (2006-2015). CONCLUSIONS: The study analyzed the trends in medical education research using SNA and analyzed their meaning using complex systems theory. During the 53-year period studied, medical education research has been subdivided and has expanded, improved, and changed along with shifts in society's needs. By analyzing the trends in medical education using the conceptual framework of complex systems theory, the research team determined that medical education is forming a sense of the voluntary order within the field of medicine by interacting with social studies, philosophy, etc., and establishing legitimacy and originality.

Exploring Research Topics and Trends in Nursing-related Communication in Intensive Care Units Using Social Network Analysis.

This study used social network analysis to identify the main research topics and trends in nursing-related communication in intensive care units. Keywords from January 1967 to June 2016 were extracted from PubMed using Medical Subject Headings terms. Social network analysis was performed using Gephi software. Research publications and newly emerging topics in nursing-related communication in intensive care units were classified into five chronological phases. After the weighting was adjusted, the top five keyword searches were "conflict," "length of stay," "nursing continuing education," "family," and "nurses." During the most recent phase, research topics included "critical care nursing," "patient handoff," and "quality improvement." The keywords of the top three groups among the 10 groups identified were related to "neonatal nursing and practice guideline," "infant or pediatric and terminal care," and "family, aged, and nurse-patient relations," respectively. This study can promote a systematic understanding of communication in intensive care units by identifying topic networks. Future studies are needed to conduct large prospective cohort studies and randomized controlled trials to verify the effects of patient-centered communication in intensive care units on patient outcomes, such as length of hospital stay and mortality.

Evaluation of Selective-Enrichment and Chromogenic Media for Salmonella Detection in Raw Shell Egg Contents with a Low Microbial Load.

The current study was conducted to evaluate the ability to recover Salmonella from shell egg contents by culture methods. A total of 4,000 eggs were obtained from a grading and packing center located in the Gyeonggi Province of South Korea, and 200 samples were created by pooling 20 broken eggs. The pooled samples were held at room temperature for 4 d before a 25-mL aliquot of each pool was added to 225 mL of modified trypticase soy broth (mTSB) and incubated at 35°C for 24 ± 2 h. A loopful of the culture was streaked onto chromogenic Druggan-Forsythe-Iversen (DFI) agar and incubated at 36 ± 1°C for 18-24 h. In addition, 1 mL and/or 0.1 mL of the mTSB cultures were added to 10 mL of Muller-Kauffmann tetrathionate with novobiocin (MKTTn) or Rappaport-Vassiliadis (RV) broth, and they were incubated for 24 ± 2 h at 35 ± 2°C or 42 ± 0.2°C, respectively. A loopful from these cultures was streaked onto Brilliant Green (BG), xylose lysine deoxycholate (XLD), and bismuth sulfite (BS) agar plates, respectively. Directly streaking onto DFI agar revealed the presence of Salmonella in 14 out of the 200 pooled samples (7%); whereas the combination of RV medium and BG, XLD, and BS agar detected the pathogen in only 9 (4.5%), 7 (3.5%), and 3 (1.5%) of the pooled samples, respectively. When MKTTn broth was used, Salmonella was detected in 7 (3.5%), 2 (1%), and 0 (0%) of the samples when streaked onto BG, XLD, and BS agar, respectively. The results indicate that direct plating onto DFI agar without enrichment was the most suitable among the methods evaluated in this study for detecting Salmonella in raw shell egg contents with a low microbial load.

Distribution and Molecular Characterization of Campylobacter Species at Different Processing Stages in Two Poultry Processing Plants.

The present study analyzed the prevalence and molecular characterization of Campylobacter at different processing steps in poultry slaughterhouses to determine where contamination mainly occurs. A total of 1,040 samples were collected at four different stages (preprocessing cloacal swabs, postevisceration, postwashing, and postchilling) in two processing plants. Campylobacter was detected in 5.8% (15 of 260) of the cloacal swabs and in 13.3% (104 of 780) of the processing samples. In both plants, the sampling points with the greatest contamination rates were after evisceration (20.5% and 15.4% for plants A and B, respectively) and significantly decreased after chilling (p < 0.05, from 20.5% to 10.9%) in plant A and after washing (from 15.4% to 2.9%) in plants B. In the result, however, the reduction in Campylobacter contamination was achieved through the sequential processing procedures in both plants. Campylobacter loads (>103 colony-forming units [CFUs]/mL) also decreased from 41.7% at evisceration to 20.0% in final carcasses. The genetic relationships of isolates were analyzed by the automated repetitive sequence-based polymerase chain reaction (rep-PCR) system, and the rep-PCR banding pattern was found to be unrelated to the processing plants, species, sampling point, or sampling day. As the gap in the intervention efficacy remains between plant A and B despite several consistencies, a national program for monitoring critical processing stages in poultry processing plants is recommended for the successful exportation of Korean-processed white mini broiler meat.

Resistance of Strains Producing Extended-Spectrum ß-Lactamases Among Salmonella from Duck Carcasses at Slaughterhouses in Three Major Provinces of South Korea.

The current study was carried out to estimate Salmonella spp. contamination of duck carcasses and to determine the antibiotic susceptibility profiles and serotype distribution of the isolates. Salmonella spp. was detected in 21.7% (26/120) of fresh raw duck carcasses sampled at different slaughterhouses in South Korea. Eight Salmonella serovars were identified; the most prevalent serovar was S. Typhimurium (34.6%), followed by S. Virchow (30.8%). All isolates were resistant to at least one antibiotic, and five remarkable isolates were resistant to more than 10 antibiotics, including third- and fourth-generation cephalosporins. Additional phenotypic and genetic characterization demonstrated that these isolates harbored resistance genes to broad-spectrum ß-lactams, blaCTX-M-15 and blaCMY-2 genes, among the most prevalent ß-lactamase enzymes worldwide. Based on molecular subtyping performed using the DiversiLab™ automated repetitive-sequence-based PCR system, isolates were classified into cluster A and cluster B. Among ß-lactamase-producing Salmonellas, the isolate showing >98% similarity in their repetitive-sequence-based PCR banding pattern seemed to have acquired the resistance gene (blaCMY-2) and thus a distinct multiresistance profile. Given that antibiotic-resistant genes might be transferred by plasmid-mediated conjugation, periodic microbiological assessment within slaughterhouses is recommended for pathogens not to be transmitted through cross-contamination during slaughtering and dressing.

Prevalence, Seasonal Occurrence, and Antimicrobial Resistance of Salmonella spp. Isolates Recovered from Chicken Carcasses Sampled at Major Poultry Processing Plants of South Korea.

The current study was conducted to assess Salmonella spp. contamination in chicken carcasses produced at major poultry processing plants in South Korea. In total, 120 chicken carcasses were collected through 12 individual trials (10 chickens per trial) from six poultry processing plants in the summer of 2014 and the winter of 2015. Eighteen chicken samples (15%) were contaminated with Salmonella, with a higher rate of contamination observed during summer (14 isolates, 11.7%) than during winter (four isolates, 3.3%). Salmonella enterica serotype Typhimurium was the most prevalent, followed by Salmonella Hadar, Salmonella Rissen, Salmonella Bareilly, and Salmonella Virchow. Among five multidrug resistant isolates, a single strain was resistant to 10 antibiotics, including third-generation cephalosporins. This cephalosporin-resistant strain exhibited the extended-spectrum ß-lactamase (ESBL) phenotype and harbored the gene encoding CTX-M-15, the most prevalent ESBL enzyme worldwide. Herein, repetitive-sequence-based polymerase chain reaction (rep-PCR) subtyping was conducted to discriminate the isolated Salmonella spp. and the ESBL-producing Salmonella isolate was distinguished by rep-PCR molecular subtyping, showing low genetic similarity in their rep-PCR-banding patterns. Given that poultry processing plants are the last stage in the chicken-production chain, the occurrence of Salmonella spp. including ESBL-producing strain in individually packaged chicken products highlights the necessity for regular monitoring for Salmonella in poultry processing plants.

Visualization of e-Health Research Topics and Current Trends Using Social Network Analysis.

BACKGROUND: E-health has been grown rapidly with significant impact on quality and safety of healthcare. However, there is a large gap between the postulated and empirically demonstrated benefits of e-health technologies and a need for a clearer mapping of its conceptual domains. Therefore, this study aimed to critically review the main research topics and trends of international e-health through social network analysis. MATERIALS AND METHODS: Medical subject heading terms were used to retrieve 3,023 research articles published from 1979 through 2014 in the PubMed database. We extracted n-grams from the corpus using a text analysis program, generated co-occurrence networks, and then analyzed and visualized the networks using Pajek software. The hub and authority measures identified the most important research topics in e-health. Newly emerging topics by 4-year period units were identified as research trends. RESULTS: The most important research topics in e-health are personal health records (PHR), health information technology, primary care, mobile health, clinical decision support systems (CDSS), and so on. The eight groups obtained through ego network analysis can be divided into four semantically different areas, as follows: information technology, infrastructure, services, and subjects. Also, four historical trends in e-health research are identified: the first focusing on e-health and telemedicine; the second, PHR and monitoring; the third, CDSS and alert; and the fourth, mobile health and health literacy. CONCLUSIONS: This study promotes a systematic understanding of e-health by identifying topic networks, thereby contributing to the future direction of e-health research and education.

Exploring the Knowledge Structure and Trends for Severe COVID-19 Risk Factors Using Text Network Analysis.

This study was aimed to identify knowledge structure and trends in severe COVID-19 risk factor using text network analysis. The 22,628 papers published during from January 2020 to December 2021. We analyzed and visualized using Text Rank analyzer and Gephi software. They were grouped into 5 central themes - biomedical factors, occupational environmental factors, demographic factors, health behavior factors, and complications. The emerging topics were identified to the chronological trends. This study can promote a systematic understanding of severe COVID-19 risk factors.

Prevalence, characterization, and antimicrobial susceptibility of Salmonella Gallinarum isolated from eggs produced in conventional or organic farms in South Korea.

To determine the prevalence of Salmonella serotype Enteritidis in eggs in South Korea, we conducted a microbiological survey of commercially available eggs produced in conventional or organic farms during the period from 2010 to 2012. The contents of 7,000 raw shell eggs (6,000 of conventional and 1,000 of organic origin) were examined to evaluate the extent and type of Salmonella Enteritidis contamination. A total of 26 salmonellae (7.4% of all pooled samples) were isolated from 350 homogenized pools, each containing the contents from 20 eggs. An unexpected and particularly surprising finding was that all the Salmonella isolates were serotyped as Salmonella Gallinarum. Salmonella Gallinarum was more common in eggs from organic farms: 10 of 50 egg pools (20.0%) from organic and 16 of 300 egg pools (5.3%) from conventional farms tested positive for Salmonella Gallinarum. However, organic and conventional isolates showed similar antimicrobial susceptibilities. All the isolates and a vaccine strain, SG 9R, which has been widely used in South Korea, were further characterized using the automated repetitive sequence-based PCR (rep-PCR) system, DiversiLab, to ascertain the molecular subtypes and to identify differences from the vaccine strain. The rep-PCR identified 2 distinct clusters among the 26 Salmonella Gallinarum isolates with a greater than 96% similarity index. These were clearly differentiated from the vaccine strain, SG 9R, with which there was a less than 86% similarity index. We found there was low genetic heterogeneity among isolates within each cluster and were able to distinguish wild type strains from the live vaccine strain (SG 9R) using the DiversiLab system.

Identification of Risk Groups for and Factors Affecting Metabolic Syndrome in South Korean Single-Person Households Using Latent Class Analysis and Machine Learning Techniques: Secondary Analysis Study.

BACKGROUND: The rapid increase of single-person households in South Korea is leading to an increase in the incidence of metabolic syndrome, which causes cardiovascular and cerebrovascular diseases, due to lifestyle changes. It is necessary to analyze the complex effects of metabolic syndrome risk factors in South Korean single-person households, which differ from one household to another, considering the diversity of single-person households. OBJECTIVE: This study aimed to identify the factors affecting metabolic syndrome in single-person households using machine learning techniques and categorically characterize the risk factors through latent class analysis (LCA). METHODS: This cross-sectional study included 10-year secondary data obtained from the National Health and Nutrition Examination Survey (2009-2018). We selected 1371 participants belonging to single-person households. Data were analyzed using SPSS (version 25.0; IBM Corp), Mplus (version 8.0; Muthen & Muthen), and Python (version 3.0; Plone & Python). We applied 4 machine learning algorithms (logistic regression, decision tree, random forest, and extreme gradient boost) to identify important factors and then applied LCA to categorize the risk groups of metabolic syndromes in single-person households. RESULTS: Through LCA, participants were classified into 4 groups (group 1: intense physical activity in early adulthood, group 2: hypertension among middle-aged female respondents, group 3: smoking and drinking among middle-aged male respondents, and group 4: obesity and abdominal obesity among middle-aged respondents). In addition, age, BMI, obesity, subjective body shape recognition, alcohol consumption, smoking, binge drinking frequency, and job type were investigated as common factors that affect metabolic syndrome in single-person households through machine learning techniques. Group 4 was the most susceptible and at-risk group for metabolic syndrome (odds ratio 17.67, 95% CI 14.5-25.3; P<.001), and obesity and abdominal obesity were the most influential risk factors for metabolic syndrome. CONCLUSIONS: This study identified risk groups and factors affecting metabolic syndrome in single-person households through machine learning techniques and LCA. Through these findings, customized interventions for each generational risk factor for metabolic syndrome can be implemented, leading to the prevention of metabolic syndrome, which causes cardiovascular and cerebrovascular diseases. In conclusion, this study contributes to the prevention of metabolic syndrome in single-person households by providing new insights and priority groups for the development of customized interventions using classification.

Obstacles to Health Big Data Utilization Based on the Perceptions and Demands of Health Care Workers in South Korea: Web-Based Survey Study.

BACKGROUND: This study focuses on the potential of health big data in the South Korean context. Despite huge data reserves and pan-government efforts to increase data use, the utilization is limited to public interest research centered in public institutions that have data. To increase the use of health big data, it is necessary to identify and develop measures to meet the various demands for such data from individuals, private companies, and research institutes. OBJECTIVE: The aim of this study was to identify the perceptions of and demands for health big data analysis and use among workers in health care-related occupations and to clarify the obstacles to the use of health big data. METHODS: From May 8 to May 18, 2022, we conducted a web-based survey among 390 health care-related workers in South Korea. We used Fisher exact test and analysis of variance to estimate the differences among occupations. We expressed the analysis results by item in frequency and percentage and expressed the difficulties in analyzing health big data by mean and standard deviation. RESULTS: The respondents who revealed the need to use health big data in health care work-related fields accounted for 86.4% (337/390); 65.6% (256/390) of the respondents had never used health big data. The lack of awareness about the source of the desired data was the most cited reason for nonuse by 39.6% (153/386) of the respondents. The most cited obstacle to using health big data by the respondents was the difficulty in data integration and expression unit matching, followed by missing value processing and noise removal. Thus, the respondents experienced the greatest difficulty in the data preprocessing stage during the health big data analysis process, regardless of occupation. Approximately 91.8% (358/390) of the participants responded that they were willing to use the system if a system supporting big data analysis was developed. As suggestions for the specific necessary support system, the reporting and provision of appropriate data and expert advice on questions arising during the overall process of big data analysis were mentioned. CONCLUSIONS: Our findings indicate respondents' high awareness of and demand for health big data. Our findings also reveal the low utilization of health big data and the need to support health care workers in their analysis and use of such data. Hence, we recommend the development of a customized support system that meets the specific requirements of big data analysis by users such as individuals, nongovernmental agencies, and academia. Our study is significant because it identified important but overlooked failure factors. Thus, it is necessary to prepare practical measures to increase the utilization of health big data in the future.

Characterization of the pathogenicity of extraintestinal pathogenic Escherichia coli isolates from pneumonia-infected lung samples of dogs and cats in South Korea.

This study aimed to investigate the pathogenicity of extraintestinal pathogenic Escherichia coli (ExPEC) isolated from dog and cat lung samples in South Korea. A total of 101 E. coli isolates were analyzed for virulence factors, phylogroups, and O-serogroups, and their correlation with bacterial pneumonia-induced mortality was elucidated. P fimbriae structural subunit (papA), hemolysin D (hlyD), and cytotoxic necrotizing factor 1 (cnf1) were highly prevalent in both species, indicating correlation with bacterial pneumonia. Phylogroups B1 and B2 were the most prevalent phylogroups (36.6% and 32.7%, respectively) and associated with high bacterial pneumonia-induced mortality rates. Isolates from both species belonging to phylogroup B2 showed high frequency of papA, hlyD, and cnf1. O-serogrouping revealed 21 and 15 serogroups in dogs and cats, respectively. In dogs, O88 was the most prevalent serogroup (n = 8), and the frequency of virulence factors was high for O4 and O6. In cats, O4 was the most prevalent serogroup (n = 6), and the frequency of virulence factors was high for O4 and O6. O4 and O6 serogroups were mainly grouped under phylogroup B2 and associated with high bacterial pneumonia-induced mortality. This study characterized the pathogenicity of ExPEC and described the probability of ExPEC pneumonia-induced mortality.

Protocol of BEYOND trial: Clinical BEnefit of sodium-glucose cotransporter-2 (SGLT-2) inhibitors in rhYthm cONtrol of atrial fibrillation in patients with diabetes mellitus.

SGLT-2 inhibitor, traditionally used for glycemic control, has several beneficial effects that can help manage heart failure (HF). SGLT-2 inhibitors reduce the risk of cardiovascular mortality in patients with HF. As atrial fibrillation (AF) is closely associated with HF and diabetes mellitus (DM) is a risk factor for AF, we assume that SGLT-2 inhibitors will also show therapeutic benefits regarding AF, especially for rhythm control. This trial has a multicenter, prospective, open, blinded endpoint design. It is a 1:1 randomized and controlled study. A total of 716 patients who are newly diagnosed of AF and DM within 1 year will be enrolled from 7 tertiary medical centers. The trial is designed to compare the effects of SGLT-2 inhibitors and other oral hypoglycemic agents on atrial rhythm control in patients with AF and DM. The primary outcome is the recurrence of AF within a year (including post-antiarrhythmic drugs (AAD) or ablation). The secondary outcomes are the ablation rate within a year, change in AF burden, size of the left atrium, NT-proBNP, the AF symptom score, and the quality of life. This trial will prospectively evaluate the effect and safety of SGLT-2 inhibitors on AF rhythm control in patients with DM. It will provide an invaluable dataset on rhythm control in AF with DM for future studies and offer novel information to assist in clinical decisions. (BEYOND trial, ClinicalTrials.gov number: NCT05029115. https://clinicaltrials.gov/ct2/show/NCT05029115).

Failure mode and effects analysis of telehealth service of minority elderly for sustainable digital transformation.

BACKGROUND: Telehealth services are time- and cost-saving solutions for disease management for older adults. Minority older individuals with multiple risk factors have an increasing demand for telehealth services. There are insufficient data on patient safety in telehealth services. This study aimed to enhance the quality of telehealth services by reducing errors and creating a safe user environment for low-income older adults. Failure mode and effects analysis tool (FMEA) was adopted to manage potential risks for sustainable digital transformation. METHOD: An eight-member multidisciplinary team conducted telehealth FMEA to determine risk priority numbers (RPNs). The process included identifying the potential cause and effect failure mode of each step; measuring severity, probability, and detectability scores for RPNs; and generating strategies to decrease potential failures. RESULTS: This study identified 24 risk factors and 34 causes in four major phases with a mean RPN of 90.7: preparation to measure biosignals, measurement of biosignals following instructions from a personal device, confirmation of measurement results, and intervention based on disease or condition type. Risk prioritization revealed four high failure modes and a total RPN of 362.7. Based on fundamental causes, risks were categorized as oblivescence, economic issues, and technology literacy. CONCLUSIONS: To correct these failure modes, stabilization of the platform, adding to the providers' manpower, and support for government policies are recommended. FMEA identifies and evaluates the potential risks of telehealth services. The selected priorities reduce the clinical risks of low-income elders who use telehealth services by weighting clinical actions.