Propolis has been used as a natural remedy in folk medicine worldwide. The antibacterial, antiviral, antifungal, and antiprotozoal aspects of its antimicrobial properties have been widely investigated. However, few studies focused on its applications in dentistry. Many dental diseases are related to various microorganisms in the oral cavity. In this study, we assessed the antimicrobial activity of Korean propolis extract, collected from 6 different regions, on oral pathogenic microorganisms. The propolis samples, collected from 6 different regions (P1: Uijeongbu, P2: Ansan, P3: Hongcheon, P4: Iksan, P5: Gwangju, and P6: Sangju), were dissolved in ethanol at two different concentrations (10 and 50 mg/ml). Three oral bacteria (Streptococcus mutans, Staphylococcus aureus, and Enterococcus faecalis) and one fungus (Candida albicans) were activated in general broth for 24 hours. Microorganisms were diluted and spread onto agar plates, onto which sterilized 6 mm filter papers with or without each propolis sample were placed. After 24 hours of incubation, clear zones of inhibition were observed. All tests were performed in triplicate. The propolis samples showed significant antibacterial and antifungal activity on oral pathogenic microorganisms; in addition, low-concentration groups showed outstanding antimicrobial efficacy on the 4 different microorganisms. Among the samples, P6 had significantly higher antibacterial activity than that of the others against three different bacteria. In particular, a high concentration of P6 showed a significant antifungal effect. In conclusion, we confirmed that Korean propolis has an inhibitory effect on oral pathogenic bacteria and fungi. Therefore, we suggest the possibility of developing oral medicine and oral care products based on Korean propolis.
Agar ; Bacteria ; Dentistry ; Enterococcus ; Ethanol ; Fungi ; Gwangju ; Gyeonggi-do ; Jeollabuk-do ; Medicine, Traditional ; Mouth ; Oral Medicine ; Propolis ; Staphylococcus aureus ; Stomatognathic Diseases

PURPOSE: This study aimed to explore public opinion on workplace bullying in the nursing field, by analyzing the keywords and topics of online news comments.METHODS: This was a text-mining study that collected, processed, and analyzed text data. A total of 89,951 comments on 650 online news articles, reported between January 1, 2013 and July 31, 2018, were collected via web crawling. The collected unstructured text data were preprocessed and keyword analysis and topic modeling were performed using R programming.RESULTS: The 10 most important keywords were “work” (37121.7), “hospital” (25286.0), “patients” (24600.8), “woman” (24015.6), “physician” (20840.6), “trouble” (18539.4), “time” (17896.3), “money” (16379.9), “new nurses” (14056.8), and “salary” (13084.1). The 22,572 preprocessed key words were categorized into four topics: “poor working environment”, “culture among women”, “unfair oppression”, and “society-level solutions”.CONCLUSION: Public interest in workplace bullying among nurses has continued to increase. The public agreed that negative work environment and nursing shortage could cause workplace bullying. They also considered nurse bullying as a problem that should be resolved at a societal level. It is necessary to conduct further research through gender discrimination perspectives on nurse workplace bullying and the social value of nursing work.
Bullying ; Data Mining ; Discrimination (Psychology) ; Nursing ; Public Opinion ; Sexism

Purpose: This study aimed to explore public opinion on workplace bullying in the nursing field, by analyzing the keywords and topics of online news comments. Methods: This was a text-mining study that collected, processed, and analyzed text data. A total of 89,951 comments on 650 online news articles, reported between January 1, 2013 and July 31, 2018, were collected via web crawling. The collected unstructured text data were preprocessed and keyword analysis and topic modeling were performed using R programming. Results: The 10 most important keywords were “work” (37121.7), “hospital” (25286.0), “patients” (24600.8), “woman” (24015.6), “physician” (20840.6), “trouble” (18539.4), “time” (17896.3), “money” (16379.9), “new nurses” (14056.8), and “salary” (13084.1). The 22,572 preprocessed key words were categorized into four topics: “poor working environment”, “culture among women”, “unfair oppression”, and “society-level solutions”. Conclusion Public interest in workplace bullying among nurses has continued to increase. The public agreed that negative work environment and nursing shortage could cause workplace bullying. They also considered nurse bullying as a problem that should be resolved at a societal level. It is necessary to conduct further research through gender discrimination perspectives on nurse workplace bullying and the social value of nursing work.

BACKGROUND: The purpose of this study was to determine whether components of the ProVent model can predict the high medical costs in Korean patients requiring at least 21 days of mechanical ventilation (prolonged mechanical ventilation [PMV]). METHODS: Retrospective data from 302 patients (61.6% male; median age, 63.0 years) who had received PMV in the past 5 years were analyzed. To determine the relationship between medical cost per patient and components of the ProVent model, we collected the following data on day 21 of mechanical ventilation (MV): age, blood platelet count, requirement for hemodialysis, and requirement for vasopressors. RESULTS: The mortality rate in the intensive care unit (ICU) was 31.5%. The average medical costs per patient during ICU and total hospital (ICU and general ward) stay were 35,105 and 41,110 US dollars (USD), respectively. The following components of the ProVent model were associated with higher medical costs during ICU stay: age <50 years (average 42,731 USD vs. 33,710 USD, p=0.001), thrombocytopenia on day 21 of MV (36,237 USD vs. 34,783 USD, p=0.009), and requirement for hemodialysis on day 21 of MV (57,864 USD vs. 33,509 USD, p<0.001). As the number of these three components increased, a positive correlation was found betweeen medical costs and ICU stay based on the Pearson's correlation coefficient (γ) (γ=0.367, p<0.001). CONCLUSION: The ProVent model can be used to predict high medical costs in PMV patients during ICU stay. The highest medical costs were for patients who required hemodialysis on day 21 of MV.
Humans ; Intensive Care Units ; Male ; Mortality ; Platelet Count ; Renal Dialysis ; Respiration, Artificial ; Retrospective Studies ; Thrombocytopenia

BACKGROUND: The purpose of this study was to determine whether components of the ProVent model can predict the high medical costs in Korean patients requiring at least 21 days of mechanical ventilation (prolonged mechanical ventilation [PMV]). METHODS: Retrospective data from 302 patients (61.6% male; median age, 63.0 years) who had received PMV in the past 5 years were analyzed. To determine the relationship between medical cost per patient and components of the ProVent model, we collected the following data on day 21 of mechanical ventilation (MV): age, blood platelet count, requirement for hemodialysis, and requirement for vasopressors. RESULTS: The mortality rate in the intensive care unit (ICU) was 31.5%. The average medical costs per patient during ICU and total hospital (ICU and general ward) stay were 35,105 and 41,110 US dollars (USD), respectively. The following components of the ProVent model were associated with higher medical costs during ICU stay: age <50 years (average 42,731 USD vs. 33,710 USD, p=0.001), thrombocytopenia on day 21 of MV (36,237 USD vs. 34,783 USD, p=0.009), and requirement for hemodialysis on day 21 of MV (57,864 USD vs. 33,509 USD, p<0.001). As the number of these three components increased, a positive correlation was found betweeen medical costs and ICU stay based on the Pearson's correlation coefficient (γ) (γ=0.367, p<0.001). CONCLUSION The ProVent model can be used to predict high medical costs in PMV patients during ICU stay. The highest medical costs were for patients who required hemodialysis on day 21 of MV.

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating downstream analysis of genome data. Bio-Express web service is freely available at https://www.bioexpress.re.kr/.