PURPOSE: The purpose of this study was to identify the characteristics of and risk factors for medical-device-related pressure ulcer (MDRPU) development in intensive care units. METHODS: A prospective cohort study design was used, and the participants were 253 adult patients who had stayed in medical and surgical intensive care units. Data were collected regarding the application of medical devices and MDRPU-related characteristics over a period of six months from June to November, 2017. Data were analyzed using independent t-test, χ2-test, Fisher's exact test, and binary logistic regression analysis with the SPSS 21.0 program. RESULTS: Among the 253 participants, MDRPUs occurred in 51 (19.8%) participants. The results of the logistic regression analysis showed that the risk factors for MDRPUs were the use of endotracheal tubes (OR=5.79, 95% CI: 1.66~20.20), having had surgery (OR=2.95, 95% CI: 1.11~7.77), being in a semi-coma/coma (OR=5.79, 95% CI: 1.04~32.05), and sedation (OR=5.54, 95% CI: 1.39~22.19). CONCLUSION: On the basis of the study results, it is effectively facilitated by nurses when they care for patients with MDRPUs in intensive care units and the results are expected to be of help in preventive education for MDRPU development as well as preparing the base data for intervention studies.
Adult ; Cohort Studies ; Critical Care ; Education ; Humans ; Intensive Care Units ; Logistic Models ; Pressure Ulcer ; Prospective Studies ; Risk Factors

PURPOSE: The purpose of this study was to identify the characteristics of and risk factors for medical-device-related pressure ulcer (MDRPU) development in intensive care units. METHODS: A prospective cohort study design was used, and the participants were 253 adult patients who had stayed in medical and surgical intensive care units. Data were collected regarding the application of medical devices and MDRPU-related characteristics over a period of six months from June to November, 2017. Data were analyzed using independent t-test, χ2-test, Fisher's exact test, and binary logistic regression analysis with the SPSS 21.0 program. RESULTS: Among the 253 participants, MDRPUs occurred in 51 (19.8%) participants. The results of the logistic regression analysis showed that the risk factors for MDRPUs were the use of endotracheal tubes (OR=5.79, 95% CI: 1.66~20.20), having had surgery (OR=2.95, 95% CI: 1.11~7.77), being in a semi-coma/coma (OR=5.79, 95% CI: 1.04~32.05), and sedation (OR=5.54, 95% CI: 1.39~22.19). CONCLUSION On the basis of the study results, it is effectively facilitated by nurses when they care for patients with MDRPUs in intensive care units and the results are expected to be of help in preventive education for MDRPU development as well as preparing the base data for intervention studies.

Chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-Seq) is a powerful technology to profile the location of proteins of interest on a whole-genome scale. To identify the enrichment location of proteins, many programs and algorithms have been proposed. However, none of the commonly used peak calling programs could accurately explain the binding features of target proteins detected by ChIP-Seq. Here, publicly available data on 12 histone modifications, including H3K4ac/me1/me2/me3, H3K9ac/me3, H3K27ac/me3, H3K36me3, H3K56ac, and H3K79me1/me2, generated from a human embryonic stem cell line (H1), were profiled with five peak callers (CisGenome, MACS1, MACS2, PeakSeq, and SISSRs). The performance of the peak calling programs was compared in terms of reproducibility between replicates, examination of enriched regions to variable sequencing depths, the specificity-to-noise signal, and sensitivity of peak prediction. There were no major differences among peak callers when analyzing point source histone modifications. The peak calling results from histone modifications with low fidelity, such as H3K4ac, H3K56ac, and H3K79me1/me2, showed low performance in all parameters, which indicates that their peak positions might not be located accurately. Our comparative results could provide a helpful guide to choose a suitable peak calling program for specific histone modifications.

Purpose: Awareness of environmental control is considered a major influence on the performance of asthma self-management behaviors that are involved in maintaining effective control of asthma. The aim of this study was to investigate whether immersive virtual reality (VR) education is effective in environmental control education for asthmatic children. Methods: Thirty asthmatic children aged 9 to 13 years with aeroallergen sensitization were enrolled. Environmental control education for asthmatic subjects were performed using either immersive VR (VR group) or conventional leaflets provided by asthma specialists (control group). Five questionnaires, such as awareness of environmental control, memory, assessment of intent to act, satisfaction test, and asthma control test (ACT) questionnaires were used for estimating the effects of education. Results: Awareness of environmental control, memory, and intent to act scores were significantly increased after education in both groups and the scores were maintained high until 4 weeks after education. In both group, ACT scores were maintained high scores before and 4 weeks after education. Satisfaction scores were very high in the VR group. Conclusion The increased scores in awareness of environmental control and intent to act indicate that the environmental control education using VR is worthy of attention as an effective educational tool for asthma management. Application of further developed techniques, including active environmental intervention by participants in VR, could be applied to effective asthma management.

Chromatin immunoprecipitation coupled with high-throughput DNA sequencing (ChIP-Seq) is a powerful technology to profile the location of proteins of interest on a whole-genome scale. To identify the enrichment location of proteins, many programs and algorithms have been proposed. However, none of the commonly used peak calling programs could accurately explain the binding features of target proteins detected by ChIP-Seq. Here, publicly available data on 12 histone modifications, including H3K4ac/me1/me2/me3, H3K9ac/me3, H3K27ac/me3, H3K36me3, H3K56ac, and H3K79me1/me2, generated from a human embryonic stem cell line (H1), were profiled with five peak callers (CisGenome, MACS1, MACS2, PeakSeq, and SISSRs). The performance of the peak calling programs was compared in terms of reproducibility between replicates, examination of enriched regions to variable sequencing depths, the specificity-to-noise signal, and sensitivity of peak prediction. There were no major differences among peak callers when analyzing point source histone modifications. The peak calling results from histone modifications with low fidelity, such as H3K4ac, H3K56ac, and H3K79me1/me2, showed low performance in all parameters, which indicates that their peak positions might not be located accurately. Our comparative results could provide a helpful guide to choose a suitable peak calling program for specific histone modifications.

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/.