1.Performance Evaluation of the ARKRAY ADAMS Bridge System Comprising Glucose GA-1171 and HbA1c HA-8180 Analyzers.
Geehay HONG ; Soo Youn LEE ; Hyung Doo PARK
Laboratory Medicine Online 2014;4(4):179-186
BACKGROUND: In diabetic patients, both glucose and hemoglobin A1c (HbA1c) concentrations are frequently measured to monitor glycemic control. We examined the analytical performance of the recently developed, automated, ADAMS bridge system (Arkray, Inc., Japan) consisting of the ADAMS glucose GA-1171 and the ADAMS HbA1c HA-8180 analyzers, which allows the consecutive measurement of glucose and HbA1c concentrations. METHODS: We evaluated precision, linearity, carry-over, effects of hematocrit, and turnaround time. Method comparison was conducted between GA-1171 and UniCel DxC 800 (Beckman Coulter, Inc., USA) and Synchron CX3 Delta (Beckman Coulter) for glucose, and between HA-8180 and HLC-723 G8 (Tosoh Bioscience, Inc., Japan) for HbA1c measurements. RESULTS: Total precision (% CV) in measuring high and low level controls was 1.11% and 1.21% for glucose using GA-1171, and 0.86% and 1.3% for HbA1c using HA-8180, respectively. In the linearity test, R2 was 0.9997, 0.9991 and 0.9973 when measuring plasma glucose (58-532 mg/dL), whole blood glucose (74-401 mg/dL), and HbA1c concentrations (4.7-14.7%), respectively. Good correlation was observed between GA-1171 and DxC 800 (r=0.9987), and between HA-8180 and HLC-723 G8 (r=0.9980). Carry-over effect was less than 0.5% for glucose and HbA1c. Turnaround time was reduced from 7 min (CX3 Delta) and 1.43 min (HLC-723 G8) to 2.16 min (GA-1171) and 1.54 min (HA-8180), respectively, when whole blood glucose and HbA1c concentrations were measured consecutively by the ADAMS bridge system. CONCLUSIONS: The ADAMS bridge system had a simple operating procedure and showed an adequate performance and a rapid turnaround time.
Blood Glucose
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Glucose*
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Hematocrit
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Humans
2.CYP21A2 Mutation Analysis in Korean Patients With Congenital Adrenal Hyperplasia Using Complementary Methods: Sequencing After Long-Range PCR and Restriction Fragment Length Polymorphism Analysis With Multiple Ligation-Dependent Probe Amplification Assay.
Geehay HONG ; Hyung Doo PARK ; Rihwa CHOI ; Dong Kyu JIN ; Jae Hyeon KIM ; Chang Seok KI ; Soo Youn LEE ; Junghan SONG ; Jong Won KIM
Annals of Laboratory Medicine 2015;35(5):535-539
CYP21A2 mutation analysis of congenital adrenal hyperplasia (CAH) is challenging because of the genomic presence of a homologous CYP21A2 pseudogene and the significant incidence of pseudogene conversion and large deletions. The objective of this study was to accurately analyze the CYP21A2 genotype in Korean CAH patients using a combination of complementary methods. Long-range PCR and restriction fragment length polymorphism analyses were performed to confirm valid amplification of CYP21A2 and to detect large gene conversions and deletions before direct sequencing. Multiple ligation-dependent probe amplification (MLPA) analysis was conducted concurrently in 14 CAH-suspected patients and six family members of three patients. We identified 27 CYP21A2 mutant alleles in 14 CAH-suspected patients. The c.293-13A>G (or c.293-13C>G) was the most common mutation, and p.Ile173Asn was the second, identified in 25% and 17.9% of alleles, respectively. A novel frame-shift mutation of c.492delA (p.Glu 164Aspfs*24) was detected. Large deletions were detected by MLPA in 10.7% of the alleles. Mutation studies of the six familial members for three of the patients aided in the identification of haplotypes. In summary, we successfully identified CYP21A2 mutations using both long-range PCR and sequencing and dosage analyses. Our data correspond relatively well with the previously reported mutation spectrum analysis.
Adrenal Hyperplasia, Congenital*
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Alleles
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Gene Conversion
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Genotype
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Haplotypes
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Humans
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Incidence
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Korea
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Polymerase Chain Reaction*
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Polymorphism, Restriction Fragment Length*
;
Pseudogenes
;
Spectrum Analysis
3.Importance of Specimen Type and Quality in Diagnosing Middle East Respiratory Syndrome.
Hee Jae HUH ; Jae Hoon KO ; Young Eun KIM ; Chang Hun PARK ; Geehay HONG ; Rihwa CHOI ; Shinae YU ; Sun Young CHO ; Ji Man KANG ; Myoung Keun LEE ; Chang Seok KI ; Eun Suk KANG ; Nam Yong LEE ; Jong Won KIM ; Yae Jean KIM ; Young Eun HA ; Cheol In KANG ; Doo Ryeon CHUNG ; Kyong Ran PECK ; Jae Hoon SONG
Annals of Laboratory Medicine 2017;37(1):81-83