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      • KCI등재

        Application of Clustering Methods for Interpretation of Petroleum Spectra from Negative-Mode ESI FT-ICR MS

        Injoon Yeo,이재원,김성환 대한화학회 2010 Bulletin of the Korean Chemical Society Vol.31 No.11

        This study was performed to develop analytical methods to better understand the properties and reactivity of petroleum,which is a highly complex organic mixture, using high-resolution mass spectrometry and statistical analysis. Ten crude oil samples were analyzed using negative-mode electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FT-ICR MS). Clustering methods, including principle component analysis (PCA),hierarchical clustering analysis (HCA), and k-means clustering, were used to comparatively interpret the spectra. All the methods were consistent and showed that oxygen and sulfur-containing heteroatom species played important roles in clustering samples or peaks. The oxygen-containing samples had higher acidity than the other samples, and the clustering results were linked to properties of the crude oils. This study demonstrated that clustering methods provide a simple and effective way to interpret complex petroleomic data.

      • KCI등재
      • Clinical Assay for AFP-L3 by Using Multiple Reaction Monitoring–Mass Spectrometry for Diagnosing Hepatocellular Carcinoma

        Kim, Hyunsoo,Sohn, Areum,Yeo, Injoon,Yu, Su Jong,Yoon, Jung-Hwan,Kim, Youngsoo American Association for Clinical Chemistry, Inc. 2018 Clinical chemistry Vol.64 No.8

        <P><B>BACKGROUND:</B></P><P><I>Lens culinaris</I> agglutinin-reactive fraction of α-fetoprotein (AFP-L3) is a serum biomarker for hepatocellular carcinoma (HCC). AFP-L3 is typically measured by liquid-phase binding assay (LiBA). However, LiBA does not always reflect AFP-L3 concentrations because of its low analytical sensitivity. Thus, we aimed to develop an analytically sensitive multiple reaction monitoring–mass spectrometry (MRM-MS) assay to quantify AFP-L3 in serum.</P><P><B>METHODS:</B></P><P>The assay entailed the addition of a stable isotope-labeled internal standard protein analog, the enrichment of AFP using a monoclonal antibody, the fractionation of AFP-L3 using <I>L. culinaris</I> agglutinin lectin, deglycosylation, trypsin digestion, online desalting, and MRM-MS analysis. The performance of the MRM-MS assay was compared with that of LiBA in 400 human serum samples (100 chronic hepatitis, 100 liver cirrhosis, and 200 HCC). Integrated multinational guidelines were followed to validate the assay for clinical implementation.</P><P><B>RESULTS:</B></P><P>The lower limit of quantification of the MRM-MS assay (0.051 ng/mL) for AFP-L3 was less than that of LiBA (0.300 ng/mL). Thus, AFP-L3, which was not observed by LiBA in HCC samples (n = 39), was detected by the MRM-MS assay, improving the clinical value of AFP-L3 as a biomarker by switching to a more analytical sensitive platform. The method was validated, meeting all the criteria in integrated multinational guidelines.</P><P><B>CONCLUSIONS:</B></P><P>Because of the lower incidence of false-negative findings, the MRM-MS assay is more suitable than LiBA for early detection of HCC.</P>

      • KCI등재

        Method Validation by CPTAC Guidelines for Multi-protein Marker Assays Using Multiple Reaction Monitoring-mass Spectrometry

        Minsoo Son,Hyunsoo Kim,Injoon Yeo,Yoseop Kim,Areum Sohn,Youngsoo Kim 한국생물공학회 2019 Biotechnology and Bioprocess Engineering Vol.24 No.2

        Quantifying multiple protein biomarkers in a blood sample at one time has many advantages for diagnosing human diseases. In this study, 34 multiplex assays by multiple reaction monitoring-mass spectrometry (MRM-MS) for serum biomarkers were characterized according to Clinical Proteomic Tumor Analysis Consortium (CPTAC) guidelines. The assays revealed that the median lower limit of quantitation (LLOQ) was 0.37 fmol/μL (16.0 ng/mL) and that the median total coefficient of variation (CV) was 18.2%, 12.2%, and 10.6% in the low-, medium-, and high-quality control (QC) samples. With regard to selectivity, the median mean differences in slope and concentration were 2.1% and 4.3%, respectively. The median values for all CVs and %difference from the nominal concentration for stability were 9.5% and 2.7% in low-QC and 3.8% and 3.1% in medium-QC. The median total CV was 9.8% in the reproducibility. Finally, 17 protein-based biomarker assays were reliable and transferrable for preclinical purposes per CPTAC guidelines.

      • Cost-Effective Automated Preparation of Serum Samples for Reproducible Quantitative Clinical Proteomics

        Lee, Jihyeon,Kim, Hyunsoo,Sohn, Areum,Yeo, Injoon,Kim, Youngsoo American Chemical Society 2019 Journal of proteome research Vol.18 No.5

        <P>Reproducible sample preparation remains a significant challenge in large-scale clinical research using selected reaction monitoring-mass spectrometry (SRM-MS), which enables a highly sensitive multiplexed assay. Although automated liquid-handling platforms have tremendous potential for addressing this issue, the high cost of their consumables is a drawback that renders routine operation expensive. Here we evaluated the performance of a liquid-handling platform in preparing serum samples compared with a standard experiment while reducing the outlay for consumables, such as tips, wasted reagents, and reagent stock plates. A total of 26 multiplex assays were quantified by SRM-MS using four sets of 24 pooled human serum aliquots; the four sets used a fixed number (1, 4, 8, or 24) of tips to dispense digestion reagents. This study demonstrated that the use of 4 or 8 tips is comparable to 24 tips (standard experiment), as evidenced by their coefficients of variation: 13.5% (for 4 and 8 tips) versus 12.0% (24 tips). Thus we can save 37% of the total experimental cost compared with the standard experiment, maintaining nearly equivalent reproducibility. The routine operation of cost-effective liquid-handling platforms can enable researchers to process large-scale samples with high throughput, adding credibility to their findings by minimizing human error.</P> [FIG OMISSION]</BR>

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