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- W2020368188 abstract "Abstract Liver cirrhosis is a worldwide health problem. Reliable, noninvasive methods for early detection of liver cirrhosis are not availabe. Using a three‐step approach, we classified sera from rats with liver cirrhosis following different treatment insults. The approach consisted of: (i) protein profiling using surface‐enhanced laser desorption/ionization (SELDI) technology; (ii) selection of a statistically significant serum biomarker set using machine learning algorithms; and (iii) identification of selected serum biomarkers by peptide sequencing. We generated serum protein profiles from three groups of rats: (i) normal ( n = 8), (ii) thioacetamide‐induced liver cirrhosis ( n = 22), and (iii) bile duct ligation‐induced liver fibrosis ( n = 5) using a weak cation exchanger surface. Profiling data were further analyzed by a recursive support vector machine algorithm to select a panel of statistically significant biomarkers for class prediction. Sensitivity and specificity of classification using the selected protein marker set were higher than 92%. A consistently down‐regulated 3495 Da protein in cirrhosis samples was one of the selected significant biomarkers. This 3495 Da protein was purified on‐chip and trypsin digested. Further structural characterization of this biomarkers candidate was done by using cross‐platform matrix‐assisted laser desorption/ionization mass spectrometry (MALDI‐MS) peptide mass fingerprinting (PMF) and matrix‐assisted laser desorption/ionization time of flight/time of flight (MALDI‐TOF/TOF) tandem mass spectrometry (MS/MS). Combined data from PMF and MS/MS spectra of two tryptic peptides suggested that this 3495 Da protein shared homology to a histidine‐rich glycoprotein. These results demonstrated a novel approach to discovery of new biomarkers for early detection of liver cirrhosis and classification of liver diseases." @default.
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- W2020368188 date "2004-10-01" @default.
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- W2020368188 title "Molecular classification of liver cirrhosis in a rat model by proteomics and bioinformatics" @default.
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- W2020368188 doi "https://doi.org/10.1002/pmic.200400839" @default.
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