Dae-Soo Moon
Chosun University
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Dae-Soo Moon.
Diagnostic Microbiology and Infectious Disease | 2014
Min Jung Lee; Sook Jin Jang; Xue Min Li; Geon Park; Joong-Ki Kook; Min Jung Kim; Young-Hyo Chang; Jong Hee Shin; Soo Hyun Kim; Dong-Min Kim; Seong-Ho Kang; Dae-Soo Moon
Since accurate identification of species is necessary for proper treatment of Acinetobacter infections, we compared the performances of 4 bacterial identification methods using 167 Acinetobacter clinical isolates to identify the best identification method. To secure more non-baumannii Acinetobacter (NBA) strains as target strains, we first identified Acinetobacter baumannii in a total of 495 Acinetobacter clinical isolates identified using the VITEK 2 system. Because 371 of 495 strains were identified as A. baumannii using gyrB multiplex 1 PCR and blaOXA51-like PCR, we performed rpoB gene sequencing and 16S rRNA gene sequencing on remaining 124 strains belonging to NBA and 52 strains of A. baumannii. For identification of Acinetobacter at the species level, the accuracy rates of rpoB gene sequencing, 16S rRNA gene sequencing, gyrB multiplex PCR, and the VITEK 2 were 98.2%, 93.4%, 77.2%, and 35.9%, respectively. The gyrB multiplex PCR seems to be very useful for the detection of ACB complex because its concordance rates to the final identification of strains of ACB complex were 100%. Both the rpoB gene sequencing and the 16S rRNA gene sequencing may be useful in identifying Acinetobacter.
Diagnostic Microbiology and Infectious Disease | 2011
Won-Young Jin; Sook-Jin Jang; Min-Jung Lee; Geon Park; Min Jung Kim; Joong-Ki Kook; Dong-Min Kim; Dae-Soo Moon; Young-Jin Park
To compare the identification accuracies of VITEK 2 (bioMérieux), MicroScan (Siemens Healthcare), and Phoenix (Becton Dickinson), microbial identification was performed on 160 clinical isolates and 50 reference strains on each of these 3 systems, using the appropriate identification kit provided by each system. Of the 142 clinical isolates that were identified at the species level, VITEK 2, MicroScan, and Phoenix correctly identified 93.7%, 82.4%, and 93.0%, and incorrectly identified 2.1%, 7.0%, and 0%, respectively. In the reference strain tests, VITEK 2, MicroScan, and Phoenix correctly identified 55.3%, 54.4%, and 78.0% of the reference strains at the species level and incorrectly identified 10.6%, 13.0%, and 6.0% of the reference strains, respectively. In conclusion, the identification rate of VITEK 2, Phoenix, and MicroScan was high or acceptable on clinical isolates. Phoenix showed a significantly higher performance than VITEK 2 or MicroScan in identifying the reference strains.
Acta Haematologica | 2015
Woo-Seong Kim; Sang-Gon Park; Geon Park; Sook-Jin Jang; Dae-Soo Moon; Seong Ho Kang
8p11 myeloproliferative syndrome (EMS) is a rare disease characterized by myeloproliferative neoplasm (MPN) associated with eosinophilia and T or B lymphoblastic lymphoma/leukemia. EMS is defined by molecular disruption of the FGFR1 gene at the 8p11-12 chromosome locus, and various partner genes are associated with FGFR1 gene translocation or insertion. The different partner-FGFR1 fusion genes are associated with slightly different disease phenotypes. The present patient showed T lymphoblastic lymphoma in a cervical lymph node, involvement of malignant lymphoma in the skin, and MPN bone marrow morphology with peripheral monocytosis. Chromosome analysis of the patient showed t(1;8)(q25;p11.2). To our knowledge, only 2 cases of EMS with translocation of t(1;8)(q25;p11.2) have been previously reported. Including this case, all 3 cases with EMS with t(1;8)(q25;p11.2) showed MPN bone marrow morphology and peripheral monocytosis. These findings support that t(1;8)(q25;p11.2) is associated with peripheral monocytosis in EMS patients. Of the 2 cases of EMS with t(1;8)(q25;p11.2) which were previously reported, FGFR1 rearrangement was not confirmed in 1 case. Similarly, FGFR1 rearrangement in the present case was not detected by fluorescence in situ hybridization or reverse transcription-polymerase chain reaction. Further study is needed to identify other techniques that could be used to demonstrate FGFR1 rearrangement.
Pharmacogenomics | 2014
Seong-Ho Kang; Geon Park; Sook Jin Jang; Dae-Soo Moon
BACKGROUND NAT2 is a common metabolizer of many clinical drugs. NAT2 haplotyping requires a complex procedure. Allele-specific PCR followed by direct sequencing or cloning sequencing are common methods used for haplotyping. However, these common methods require labor-intensive procedures. Allele-specific sequencing was designed for haplotyping of the NAT2 gene. MATERIALS & METHODS Using rapid DNA polymerase with high fidelity, we amplified the NAT2 coding region of genomic DNA for direct sequencing, allele-specific sequencing and for the cloning of genomic DNA from 307 healthy Korean subjects. Direct sequencing analysis of the 870-bp coding region of NAT2 was performed in order to search 11 of the most common SNPs. For cases who were heterozygous for two or more SNPs and whose haplotypes were not determined by direct sequencing, we performed sequencing analysis using the allele-specific sequencing primer for one specified allele. We performed cloning-sequencing analysis for confirmation of the haplotyping results of allele-specific sequencing. RESULTS Homozygotes for SNPs, heterozygotes for one SNP and heterozygotes for two or more SNPs were 142 (46.3%), six (2.0%) and 259 (51.8%) cases, respectively. There was 100% concordance between the results of NAT2 haplotyping using allele-specific sequencing and cloning sequencing of 65 cases that were heterozygous for two or more SNPs in 307 samples. For cases that were homozygous for the SNPs by direct sequencing, the haplotypes of NAT2 were clearly determined by cloning sequencing. CONCLUSION We have developed a novel method for NAT2 haplotyping using allele-specific sequencing, which could be an innovative and reliable method for NAT2 haplotyping.
Japanese Journal of Infectious Diseases | 2005
Sook-Jin Jang; Hu-Lin Han; Sung-Hyun Lee; So-Yeon Ryu; Bidur Prasad Chaulagain; Young-Lae Moon; Dong-Hui Kim; Ok-Yeon Jeong; Jong-Hee Shin; Dae-Soo Moon; Young-Jin Park
Korean Journal of Laboratory Medicine | 2003
Jin-Hee Kim; Sook-Jin Jang; Dae-Soo Moon; Young-Jin Park
Korean Journal of Laboratory Medicine | 2005
Sung-Hyun Lee; Gyun-Yeol Ahn; Ok-Yeon Jeong; Young-Jin Park; Sook-Jin Jang; Dae-Soo Moon
Korean Journal of Laboratory Medicine | 2005
Sung-Hyun Lee; Sook-Jin Jang; Dae-Soo Moon; Young-Jin Park; Gyun-Yeol Ahn; Hu-Lin Han; Bidur Prasad Chaulagain; Ok-Yeon Jeong
Korean Journal of Laboratory Medicine | 2002
Geon Park; Young-Jin Park; Sook-Jin Jang; Dae-Soo Moon
Laboratory Medicine Online | 2018
Ung-Jun Kim; Ho-Jong Lee; In-Sun Choi; Seong-Ho Kang; Sook-Jin Jang; Dae-Soo Moon; Geon Park