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.

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).

The critical role of job embeddedness: The impact of psychological empowerment and learning orientation on organizational commitment.

Job embeddedness (JE) has been recognized as a key factor to address the issue of employee turnover and employee attitudes. This study explores underlying mechanisms of job embeddedness that link the organizational environment and the individuals' perceptions of the job. Particularly, the effects of psychological empowerment and learning orientation on organizational commitment were examined. This study hypothesizes that psychological empowerment (PE) and learning orientation (LO) should influence organizational commitment (OC) and job embeddedness plays a significant mediating role in these relationships. Data were collected from 27 offices of Human Resource Development Service of Korea (governmental agency) located in major cities in South Korea. Results indicate that all hypothesized relationships (PE and JE, LO and JE, LO and OC, JE and OC, and the mediating role of JE) are supported, except for psychological empowerment and organizational commitment. While the impact of psychological empowerment was not significantly related to organizational commitment, it is notable that through job embeddedness, psychological empowerment had indirect effects on organizational commitment. Further, learning orientation had significant effects on job embeddedness and organizational commitment. Lastly, the most compelling finding is a full mediation of job embeddedness in the relationship between psychological empowerment and organization commitment. Implications for research and practice are discussed.

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.

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.

Properties of broiler breast meat with pale color and a new approach for evaluating meat freshness in poultry processing plants.

The current trend in monitoring meat quality is to move the quality measurements from the laboratory to the processing line. To provide better meat quality control in the commercial poultry processing plants, we evaluated the quality of broiler breast meat samples, observing different colors, and assessed their freshness using a Torrymeter. Different colors were classified based on the mean ± standard deviation of lightness (L*) values in 1,499 broiler breast fillets: Dark (L* < 56), normal (56 ≤ L* ≤ 62), and pale (L* > 62). To characterize the differences between the pale and normal color groups, we evaluated additional fillets for meat quality traits. Changes in meat quality during storage were also evaluated. The L* and Torrymeter values (freshness values) allowed us to distinguish between the pale and normal meat samples. Normal and pale fillets showed a significant difference in pH, Torrymeter values, and water-holding capacity (P < 0.001). The L* values were significantly correlated with cook and drip loss (P < 0.01) and were higher (paler, +1.2 L* unit) at 72-h postmortem than at 4-h postmortem. Torrymeter values were correlated with cook loss (P < 0.05) and pH (P < 0.001), and significantly decreased with the increase in storage period (P < 0.001). These results suggest the applicability of the Torrymeter, a fast and non-destructive device, in distinguishing stale and fresh breast fillets. With its portability and simplicity, the Torrymeter is expected to be a valuable tool to estimate meat freshness. Especially, the use of Torrymeter for evaluating pale breast fillets may allow easy identification and separation of fillets according to their pale, soft, and exudative properties in commercial poultry processing lines.

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.

Identifying the Risk Factors Associated with Nursing Home Residents' Pressure Ulcers Using Machine Learning Methods.

BACKGROUND: Machine learning (ML) can keep improving predictions and generating automated knowledge via data-driven predictors or decisions. OBJECTIVE: The purpose of this study was to compare different ML methods including random forest, logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM in terms of their accuracy, sensitivity, specificity, negative predictor values, and positive predictive values by validating real datasets to predict factors for pressure ulcers (PUs). METHODS: We applied representative ML algorithms (random forest, logistic regression, linear SVM, polynomial SVM, radial SVM, and sigmoid SVM) to develop a prediction model (N = 60). RESULTS: The random forest model showed the greatest accuracy (0.814), followed by logistic regression (0.782), polynomial SVM (0.779), radial SVM (0.770), linear SVM (0.767), and sigmoid SVM (0.674). CONCLUSIONS: The random forest model showed the greatest accuracy for predicting PUs in nursing homes (NHs). Diverse factors that predict PUs in NHs including NH characteristics and residents' characteristics were identified according to diverse ML methods. These factors should be considered to decrease PUs in NH residents.

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.

A Technology-Mediated Interventional Approach to the Prevention of Metabolic Syndrome: A Systematic Review and Meta-Analysis.

BACKGROUND: Technology-mediated interventions help overcome barriers to program delivery and spread metabolic syndrome prevention programs on a large scale. A meta-analysis was performed to evaluate the impact of these technology-mediated interventions on metabolic syndrome prevention. METHODS: In this meta-analysis, from 30 January 2018, three databases were searched to evaluate interventions using techniques to propagate diet and exercise lifestyle programs for adult patients with metabolic syndrome or metabolic risk. RESULTS: Search results found 535 citations. Of these, 18 studies met the inclusion criteria analyzed in this article. The median duration of intervention was 4 months and the follow-up period ranged from 1.5 to 30 months. The standardized mean difference (SMD) between the two groups was waist circumference -0.35 (95% CI -0.54, -0.15), triglyceride -0.14 (95% CI -0.26, -0.03), fasting blood glucose -0.31 (95% CI -0.42, -0.19), body weight -1.34 (95% CI -2.04, -0.64), and body mass index -1.36 (95% CI -2.21, -0.51). There was no publication bias in this study. CONCLUSION: Technology-mediated intervention improved clinically important metabolic syndrome related indicators such as excess body fat around the waist, fasting glucose, and body mass index. These interventions will play an important role in the dissemination of metabolic syndrome prevention programs.

Application of Machine Learning Methods in Nursing Home Research.

Background: A machine learning (ML) system is able to construct algorithms to continue improving predictions and generate automated knowledge through data-driven predictors or decisions. Objective: The purpose of this study was to compare six ML methods (random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM) of predicting falls in nursing homes (NHs). Methods: We applied three representative six-ML algorithms to the preprocessed dataset to develop a prediction model (N = 60). We used an accuracy measure to evaluate prediction models. Results: RF was the most accurate model (0.883), followed by the logistic regression model, SVM linear, and polynomial SVM (0.867). Conclusions: RF was a powerful algorithm to discern predictors of falls in NHs. For effective fall management, researchers should consider organizational characteristics as well as personal factors. Recommendations for Future Research: To confirm the superiority of ML in NH research, future studies are required to discern additional potential factors using newly introduced ML methods.

A Prospective Analysis of Health Information Portals in Four Years.

OBJECTIVES: We examined 17 health information portals to determine the status of web-based health information services in the United States (U.S.A.), South Korea, the United Kingdom (U.K.), and Australia. METHODS: We analyzed longitudinal trends in 35 items of online health information over four years among representative health information portals (eight based in the U.S.A., seven in South Korea, one in the U.K., and one in Australia), focusing on external portal structure, content scope, service characteristic, and service function with four stakeholder groups of six stakeholders. RESULTS: The most notable change was in the service items, and overall, in 44.1% of total items: 17.6% in service characteristic, 41.2% in external portal structure, 58.8% in service function, and 58.8% in content scope change. More specifically, these changes included increases in the "mobile application utility" (service function), "use of personal health records" on public health portals (content scope change), "Charts and videos" (service characteristic), and "renewal date" (external portal structure). CONCLUSIONS: This review of existing health portals will be a footnote for enabling health care providers to confirm whether the needs of consumers are reflected on their website with high reliability. Furthermore, these findings will help to enhance the quality of portals by delivering relevant information to stakeholders and to the consumers of online health information.

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.

The Effects of Laughter Therapy for the Relief of Employment-Stress in Korean Student Nurses by Assessing Psychological Stress Salivary Cortisol and Subjective Happiness.

OBJECTIVES: Korean student nurses may be exposed to stress caused by their future employment (employment stress). The aim of this study was to investigate the effects of a Laughter Program on psychological stress, by assessing salivary cortisol and the subjective happiness of student nurses in order to relieve employment stress. METHODS: A quasi-experimental, non-equivalent, control-group, and pre-test/post-test was conducted in 4th year student nurses (n = 48) from 2 universities in Korea at a time when participants' final exams and job searches were simultaneously occurring. Physiological stress (salivary cortisol), and psychological stress measured using modified Cornell Medical Index questionnaire and the Subjective Happiness Scale were used to determine the effects of the program. RESULTS: The results of the study showed that the Laughter Program was effective in relieving employment stress and increasing the subjective well-being of student nurses. Psychological stress (p < 0.001), salivary cortisol levels (p < 0.001), and subjective happiness (p < 0.001) were statistically significantly improved after the intervention compared with before the Laughter Program. CONCLUSION: This study is an effective evidence-based intervention to reduce student nurses employment stress and improve subjective happiness.

Outbreak of African swine fever in South Korea, 2019.

African swine fever, a fatal haemorrhagic disease of swine, was confirmed in domestic pigs for the first time in South Korea in September 2019. The causative virus belonged to the p72 genotype II and had an additional tandem repeat sequence in the intergenic region (IGR) between the I73R and I329L.

Comparison of the effect of propofol and desflurane on S-100ß and GFAP levels during controlled hypotension for functional endoscopic sinus surgery: A randomized controlled trial.

BACKGROUND: Although surgical field visualization is important in functional endoscopic sinus surgery (FESS), the complications associated with controlled hypotension for surgery should be considered. Intraoperative hypotension is associated with postoperative stroke, leading to subsequent hypoxia with potential neurologic injury. We investigated the effect of propofol and desflurane anesthesia on S-100ß and glial fibrillary acidic protein (GFAP) levels which are early biomarkers for cerebral ischemic change during controlled hypotension for FESS. METHODS: For controlled hypotension during FESS, anesthesia was maintained with propofol/remifentanil in propofol group (n = 30) and with desflurane/remifentanil in desflurane group (n = 30). For S-100ß and GFAP assay, blood samples were taken at base, 20 and 60 minutes after achieving the target range of mean arterial pressure, and at 60 minutes after surgery. RESULTS: The base levels of S-100ß were 98.04 ±â€Š78.57 and 112.61 ±â€Š66.38 pg/mL in the propofol and desflurane groups, respectively. The base levels of GFAP were 0.997 ±â€Š0.486 and 0.898 ±â€Š0.472 ng/mL in the propofol and desflurane groups, respectively. The S-100ß and GFAP levels were significantly increased in the study period compared to the base levels in both groups (P ≤ .001). There was no significant difference at each time point between the 2 groups. CONCLUSION: On comparing the effects of propofol and desflurane anesthesia for controlled hypotension on the levels of S-100ß and GFAP, we noted that there was no significant difference in S-100ß and GFAP levels between the 2 study groups. CLINICAL TRIAL REGISTRATION: Available at: http://cris.nih.go.kr, KCT0002698.

Dendropanax morbiferus leaf extract facilitates oligodendrocyte development.

Treatment of multiple sclerosis is effective when anti-inflammatory, neuroprotective and regenerative strategies are combined. Dendropanax morbiferus (DM) has anti-inflammatory, anti-oxidative properties, which may be beneficial for multiple sclerosis. However, there have been no reports on the effects of DM on myelination, which is critical for regenerative processes. To know whether DM benefits myelination, we checked differentiation and myelination of oligodendrocytes (OLs) in various primary culture systems treated with DM leaf EtOH extracts or control. DM extracts increased the OL membrane size in the mixed glial and pure OL precursor cell (OPC) cultures and changed OL-lineage gene expression patterns in the OPC cultures. Western blot analysis of DM-treated OPC cultures showed upregulation of MBP and phosphorylation of ERK1/2. In myelinating cocultures, DM extracts enhanced OL differentiation, followed by increased axonal contacts and myelin gene upregulations such as Myrf, CNP and PLP. Phytochemical analysis by LC-MS/MS identified multiple components from DM extracts, containing bioactive molecules such as quercetin, cannabidiol, etc. Our results suggest DM extracts enhance OL differentiation, followed by an increase in membrane size and axonal contacts, thereby indicating enhanced myelination. In addition, we found that DM extracts contain multiple bioactive components, warranting further studies in relation to finding effective components for enhancing myelination.