조용근 교수

Although tuberculosis (TB) is a severe health problem worldwide, the current diagnostic methods are far from optimal. Metabolomics is increasingly being used in the study of infectious diseases. We performed metabolome profiling to identify potential biomarkers in patients with active TB. Serum samples from 21 patients with active pulmonary TB, 20 subjects with latent TB infection (LTBI), and 28 healthy controls were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) follow

BACKGROUND: We aimed to determine the major contributing component of metabolic syndrome (MetS) that results in an elevated small dense LDL cholesterol (sdLDL-C) concentration and sdLDL-C/LDL-C ratio. METHODS: Four hundred and forty-seven subjects (225 men; 222 women) with MetS were randomly selected from the Korean Metabolic Syndrome Research Initiatives-Seoul cohort study. Age- and sex-matched healthy controls (181 men; 179 women) were also randomly selected from the same cohort. RESULTS: A co

Background Bile acids (BAs) have been demonstrated to exert a variety of metabolic effects and alterations in BAs have been reported in patients with obesity, insulin resistance (IR) and type 2 diabetes mellitus (T2DM). However, it is unclear which metabolic condition is the main contributor to alterations in BAs. In this study, we investigate the associations between different BA profiles with glycemia, obesity or IR status. Methods Fasting serum concentrations of 15 BA species were determined

Thalassemia is the most common form of hereditary anemia. Here, we aimed to investigate the 13-year trend of the epidemiologic profiles and risk of comorbidities in thalassemia using a nationwide population-based registry in Korea. Diagnosis of thalassemia, the comorbidities and transfusion events in patients with thalassemia were identified in the Korean National Health Insurance database, which includes the entire population. The prevalence of thalassemia increased from 0.74/100,000 in 2006 to

This study highlights the importance of CA-RVs diagnosis in transplant recipients even in long-term posttransplant period, and in non-transplant critically ill patients in ICU with MV.

We evaluated quantitative hepatitis B surface antigen (qHBsAg) assays and a hepatitis B virus (HBV) core-related antigen (HBcrAg) assay. A total of 529 serum samples from patients with hepatitis B were tested. HBsAg levels were determined by using the Elecsys (Roche Diagnostics, Indianapolis, IN) and Architect (Abbott Laboratories, Abbott Park, IL) qHBsAg assays. HBcrAg was measured by using Lumipulse HBcrAg assay (Fujirebio, Tokyo, Japan). Serum aminotransferases and HBV DNA were respectively q

A novel multiplex real-time PCR assay for concurrent detection of hepatitis viruses was evaluated for its clinical performance in screening patients with acute hepatitis. A total of 648 serum samples were collected from patients with acute symptoms of hepatitis. Concurrent detection of nucleic acids of HAV, HBV and HCV was performed using the Magicplex™ HepaTrio Real-time Detection test. Serum nucleic acid levels of HBV and HCV were also quantified by the Cobas® AmpliPrep/Cobas® TaqMan® (CAP/CTM

Li-Fraumeni syndrome (LFS) is a rare, inherited syndrome associated with increased risk of various early-onset tumors. Since the introduction of classic LFS criteria, various criteria have been proposed to include patients with incomplete LFS features, which make up Li-Fraumeni-like syndromes (LFL). Germline missense mutations of TP53 are the primary cause of LFS and LFL. Mutations mostly reside in the DNA-binding domain of the gene and have a dominant-negative effect (DNE) over alternate wild-t

We read with great interest the article by Srisawasdi et al 1 regarding a new formula for the estimation of small, dense, low-density lipoprotein cholesterol (sdLDL-C) levels. The investigators studied the correlation between classic lipid measures and sdLDL-C measured directly via a newly developed homogeneous method, in a total of 220 subjects without liver or renal disease. Using the data, they deduced the following simple equation for calculating sdLDL-C: sdLDL-C mg/dL = 0.580 (non-HDL-C) +