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문선정,장해나,Moon, Sun Jung,Jang, Haena 한국중환자간호학회 2022 중환자간호학회지 Vol.15 No.3
Purpose : This study aimed to identify the factors influencing the professional quality of life of intensive care unit (ICU) nurses working in university hospitals. Methods : A survey was conducted on 171 ICU nurses in university hospitals in B City, South Korea. This study used the Professional Quality of Life instrument, which consists of three subscales, namely, compassion satisfaction, burnout, and secondary traumatic stress. Data were analyzed using stepwise multiple regression analysis. Results : Compassion satisfaction was influenced by resilience, ICU job satisfaction, and innovation-oriented culture, and these variables explained 37.1% of the variance in compassion satisfaction. Burnout was influenced by resilience, a hierarchy-oriented culture, and ICU job satisfaction, and these variables explained 42.9% of the variance in burnout. Secondary traumatic stress was influenced by a task-oriented culture and resilience, and these variables explained 12.5% of the variance in secondary traumatic stress. Conclusion : These findings suggest the importance of improving resilience and job satisfaction to enhance the professional quality of life in ICU nurses. Moreover, creating an innovation-oriented culture rather than a hierarchical and task-oriented culture can effectively improve the professional quality of life of ICU nurses.
Choi, Hyun Ho,Kang, Moon Sung,Kim, Min,Kim, Haena,Cho, Jeong Ho,Cho, Kilwon WILEY‐VCH Verlag 2013 Advanced functional materials Vol.23 No.6
<P><B>Abstract</B></P><P>A novel strategy for analyzing bias‐stress effects in organic field‐effect transistors (OFETs) based on a four‐parameter double stretched‐exponential formula is reported. The formula is obtained by modifying a traditional single stretched‐exponential expression comprising two parameters (a characteristic time and a stretched‐exponential factor) that describe the bias‐stress effects. The expression yields two characteristic times and two stretched‐exponential factors, thereby separating out the contributions due to charge trapping events in the semiconductor layer‐side of the interface and the gate‐dielectric layer‐side of the interface. The validity of this method was tested by designing two model systems in which the physical properties of the semiconductor layer and the gate‐dielectric layer were varied systematically. It was found that the gate‐dielectric layer, in general, plays a more critical role than the semiconductor layer in the bias‐stress effects, possibly due to the wider distribution of the activation energy for charge trapping. Furthermore, the presence of a self‐assembled monolayer further widens the distribution of the activation energy for charge trapping in gate‐dielectric layer‐side of the interface and causes the channel current to decay rapidly in the early stages. The novel analysis method presented here enhances our understanding of charge trapping and provides rational guidelines for developing efficient OFETs with high performance.</P>
Effect of blood pressure and glycemic control on the plasma cell-free DNA in hemodialysis patients
( Da Wun Jeong ),( Ju Young Moon ),( Young Wook Choi ),( Haena Moon ),( Kipyo Kim ),( Yu Ho Lee ),( Se Yeun Kim ),( Yang Gyun Kim ),( Kyung Hwan Jeong ),( Sang Ho Lee ) 대한신장학회 2015 Kidney Research and Clinical Practice Vol.34 No.4
Background: The plasma levels of cell-free DNA (cfDNA) are known to be elevated under inflammatory or apoptotic conditions. Increased cfDNA levels have been reported in hemodialysis (HD) patients. The aim of this study was to investigate the clinical significance of cfDNA in HD patients. Methods: A total of 95 patients on HD were enrolled. We measured their predialysis cfDNA levels using real-time EIF2C1 gene sequence amplification and analyzed its association with certain clinical parameters. Results: The mean plasma cfDNA level in the HD patients was 3,884 ± 407 GE/mL, and the mean plasma cfDNA level in the control group was 1,420 ± 121 GE/mL (P < 0.05). Diabetic patients showed higher plasma cfDNA levels compared with nondiabetic patients (P < 0.01). Patients with cardiovascular complications also showed higher plasma cfDNA levels compared with those without cardiovascular complication (P < 0.05). In univariable analysis, the cfDNA level was associated with 3-month mean systolic blood pressure (SBP), white blood cell, serum albumin, creatinine (Cr), normalized protein catabolic rate in HD patients. In diabetic patients, it was significantly correlated with SBP, hemoglobin A1c, and serum albumin. In multivariate analysis, SBP was the independent determinant for the cfDNA level. In diabetic patients, cfDNA level was independently associated with hemoglobin A1c and SBP. Conclusions: In patients with HD, cfDNA is elevated in diabetic patients and patients with cardiovascular diseases. Uncontrolled hypertension and poor glycemic control are independent determinants for the elevated cfDNA. Our data suggest that cfDNA might be a marker of vascular injury rather than proinflammatory condition in HD patients.
Evaluation of Digital PCR as a Technique for Monitoring Acute Rejection in Kidney Transplantation
Lee, Hyeseon,Park, Young-Mi,We, Yu-Mee,Han, Duck Jong,Seo, Jung-Woo,Moon, Haena,Lee, Yu-Ho,Kim, Yang-Gyun,Moon, Ju-Young,Lee, Sang-Ho,Lee, Jong-Keuk Korea Genome Organization 2017 Genomics & informatics Vol.15 No.1
Early detection and proper management of kidney rejection are crucial for the long-term health of a transplant recipient. Recipients are normally monitored by serum creatinine measurement and sometimes with graft biopsies. Donor-derived cell-free deoxyribonucleic acid (cfDNA) in the recipient's plasma and/or urine may be a better indicator of acute rejection. We evaluated digital PCR (dPCR) as a system for monitoring graft status using single nucleotide polymorphism (SNP)-based detection of donor DNA in plasma or urine. We compared the detection abilities of the QX200, RainDrop, and QuantStudio 3D dPCR systems. The QX200 was the most accurate and sensitive. Plasma and/or urine samples were isolated from 34 kidney recipients at multiple time points after transplantation, and analyzed by dPCR using the QX200. We found that donor DNA was almost undetectable in plasma DNA samples, whereas a high percentage of donor DNA was measured in urine DNA samples, indicating that urine is a good source of cfDNA for patient monitoring. We found that at least 24% of the highly polymorphic SNPs used to identify individuals could also identify donor cfDNA in transplant patient samples. Our results further showed that autosomal, sex-specific, and mitochondrial SNPs were suitable markers for identifying donor cfDNA. Finally, we found that donor-derived cfDNA measurement by dPCR was not sufficient to predict a patient's clinical condition. Our results indicate that donor-derived cfDNA is not an accurate predictor of kidney status in kidney transplant patients.