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Kim, Young-Jin,Park, Junbeom,Jeong, Hyeon Su,Park, Min,Baik, Seulki,Lee, Dong Su,Rho, Heesuk,Kim, Hyungjun,Lee, Joong Hee,Kim, Seung-Min,Kim, Young-Kwan The Royal Society of Chemistry 2019 Nanoscale Vol.11 No.12
<P>The seed-mediated growth strategy of Au nanoparticles (Au NPs) inside carbon nanotube (CNT) fibers is demonstrated to greatly improve their mechanical and electrical properties and provide a function for catalytic applications. The resulting Au NP@CNT nanocomposite fibers exhibit 100% knot efficiency, catalytic activity and considerably enhanced modulus, tensile strength, and electrical conductivity from 7 GPa, 109 MPa and 1300 S cm<SUP>−1</SUP> to 24 GPa, 351 MPa and 3600 S cm<SUP>−1</SUP>, respectively. The enhancement mechanism is also revealed by systematic characterization and theoretical simulations.</P>
Analysis of EEG to quantify depth of anesthesia using Hidden Markov Model.
Kim, Junbeom,Hyub, Huh,Yoon, Seung Zhoo,Choi, Ho-Jin,Kim, Kwang Moo,Park, Sang-Hyun IEEE Service Center 2014 Conference proceedings Vol.36 No.-
<P>Real-time quantification of the patient's consciousness level during anesthesia is an important issue to avoid intraoperative awareness and post-operative side effects. A depth-of-anesthesia (DoA) monitoring method called Bispectral Index (BIS) is generally used for this purpose. However, BIS is known to be inaccurate at the transitory state, and also shows a critical time delay in quantifying the patient's consciousness level. This paper introduces a novel method to reduce the response time in the quantification process. This thesis develops a new index called HDoA by analyzing EEG using Hidden Markov Model. The proposed approach is composed by two steps, training and testing. In the training step, two HMM, awakened and anesthetized model are learned based on each training set. In the testing step, by evaluating the probability of producing the testing EEG from two models respectively, the index HDoA is derived. Since the evaluation of DoA using HMM is training based method, it have better performance with more training process. Experiments show that HDoA has a high correlation with BIS at a steady state, and outperforms BIS in two ways: (1) shorter delay time in transition state, and (2) higher Fisher Score. The validity of HDoA has been tested by 8 real clinical data.</P>
Kim, Byeong-Keuk,Kim, Jung-Sun,Park, Junbeom,Ko, Young-Guk,Choi, Donghoon,Jang, Yangsoo,Hong, Myeong-Ki Yonsei University College of Medicine 2012 Yonsei medical journal Vol.53 No.3
<P><B>Purpose</B></P><P>There is a lack of sufficient data in comparison of optical coherence tomographic (OCT) findings between first- and second-generation drug-eluting stents (DES). Compared to first-generation (i.e., sirolimus- or paclitaxel-eluting stents), second-generation DESs (i.e., everolimus- or biolinx-based zotarolimus-eluting stents) might have more favorable neointimal coverage.</P><P><B>Materials and Methods</B></P><P>Follow-up OCT findings of 103 patients (119 lesions) treated with second-generation DESs were compared with those of 139 patients (149 lesions) treated with first-generation DESs. The percentage of uncovered or malapposed struts, calculated as the ratio of uncovered or malapposed struts to total struts in all OCT cross-sections, respectively, was compared between the two groups.</P><P><B>Results</B></P><P>Both DES groups showed similar suppression of neointimal hyperplasia (NIH) on OCT (mean NIH cross-sectional area; second- vs. first-generation=1.1±0.5 versus 1.2±1.0 mm<SUP>2</SUP>, respectively, <I>p</I>=0.547). However, the percentage of uncovered struts of second-generation DESs was significantly smaller than that of first-generation DESs (3.8±4.8% vs.7.5±11.1%, respectively, <I>p</I><0.001). The percentage of malapposed struts was also significantly smaller in second-generation DESs than in first-generation DESs (0.4±1.6% vs.1.4±3.7%, respectively, <I>p</I>=0.005). In addition, intra-stent thrombi were less frequently detected in second-generations DESs than in first-generation DESs (8% vs. 20%, respectively, <I>p</I>=0.004).</P><P><B>Conclusion</B></P><P>This follow-up OCT study showed that second-generation DESs characteristically had greater neointimal coverage than first-generation DESs.</P>
Kim, Daehoon,Yang, Pil-Sung,Kim, Tae-Hoon,Uhm, Jae-Sun,Park, Junbeom,Pak, Hui-Nam,Lee, Moon-Hyoung,Joung, Boyoung UNKNOWN 2018 CIRCULATION JOURNAL Vol.82 No.8
<P>Conclusions: Comorbid AF in patients with osteoporosis was associated with an increased risk of bone fracture and death after fracture.</P>
The Detection of Urinary Exosomal miRNAs for Cancer Diagnostics and Prognostics
Kim Junbeom,Kim Mina,Kang Ji Yoon,봉기완,최낙원 한국바이오칩학회 2023 BioChip Journal Vol.17 No.3
Liquid biopsy is a non-invasive diagnostic method that utilizes the detection and analysis of biomarkers in bodily fl uids for the diagnosis and prognosis of various diseases. Exosomal microRNAs (miRNAs), which are remarkably stable because of the protection by extracellular vesicles (EVs), in liquid biopsy have gained considerable attention as they can regulate gene expression and serve as crucial biomarkers for the presence and progression of diff erent types of cancer. Especially, urinary exosomal miRNAs have shown promising results as biomarkers for cancer from the urogenital system, such as blad- der, renal tubular, and prostate. However, there are still signifi cant challenges hindering the clinical utilization of urinary exosomal miRNA biomarkers, including improper sample handling, and data bias. Therefore, the detection procedure of urinary exosomal miRNAs needs to be standardized and thoroughly reviewed to ensure accurate and reliable clinical results. In this review, we have explored various detection procedures for urinary exosomal miRNAs, which have great potential as biomarkers for cancer diagnostics and prognostics.
Junbeom Kim(김준범),KyoJoong Oh(오교중),Keun-Whee Oh(오근휘),Ho-Jin Choi(최호진) 한국정보과학회 2011 한국정보과학회 학술발표논문집 Vol.38 No.1C
This research deals with an issue of preventive medicine in bioinformatics. We can diagnose liver conditions reasonably well to prevent Liver Cirrhosis by classifying liver disorder patients into fatty liver and high risk groups. The classification proceeds in two steps. Classification rules are first built by clustering five attributes (MCV, ALP, ALT, ASP, and GGT) of blood test dataset provided by the UCI Repository. The clusters can be formed by the K-mean method that analyzes multi dimensional attributes. We analyze the properties of each cluster divided into fatty liver, high risk and normal classes. The classification rules are generated by the analysis. In this paper, we suggest a method to diagnosis and predict liver condition to alcoholic patient according to risk levels using the classification rule from the new results of blood test. The K-mean classifier has been found to be more accurate for the result of blood test and provides the risk of fatty liver to normal liver conditions.