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245 tr. ; 21 cm

Pretreatment of Whole Blood for Use in Immunochromatographic Assays for Hepatitis B Virus Surface Antigen

Immunochromatographic assays (ICAs) are also referred to as rapid tests, since they are simple and the results can be obtained within minutes after manually loading a few drops of a sample into each sample well of the test device. However, whole blood cannot be tested with ICA kits due to the visual hindrance caused by the color of red blood cells (RBCs), unless a cell-removing device such as a filter is mounted on the kits. Thus, when testing with blood, the advantage of the ICA kit is lost because of the additional time and machines required to coagulate and separate whole blood before preparing the serum. To overcome this limitation, whole-blood samples were added to a pretreatment solution to decolor the RBCs; the resulting mixtures were then loaded into the sample wells of the test device. The pretreating solution was composed of hydrogen peroxide (H(2)O(2)) to decolor the RBCs, Sag 471 (Osi Specialties) to restrain the mixture from vigorous foaming, sodium azide (NaN(3)) to inhibit the enzyme, which generates excessive foam at the beginning of decolorization, and EDTA as a chelating agent. As a result of this pretreatment, whole blood could be used with the ICA kit without reducing its simplicity and rapidity

Applying of Hierarchical Clustering to Analysis of Protein Patterns in the Human Cancer-Associated Liver

<div><p>Background</p><p>There are two ways that statistical methods can learn from biomedical data. One way is to learn classifiers to identify diseases and to predict outcomes using the training dataset with established diagnosis for each sample. When the training dataset is not available the task can be to mine for presence of meaningful groups (clusters) of samples and to explore underlying data structure (unsupervised learning).</p><p>Results</p><p>We investigated the proteomic profiles of the cytosolic fraction of human liver samples using two-dimensional electrophoresis (2DE). Samples were resected upon surgical treatment of hepatic metastases in colorectal cancer. Unsupervised hierarchical clustering of 2DE gel images (n = 18) revealed a pair of clusters, containing 11 and 7 samples. Previously we used the same specimens to measure biochemical profiles based on cytochrome P450-dependent enzymatic activities and also found that samples were clearly divided into two well-separated groups by cluster analysis. It turned out that groups by enzyme activity almost perfectly match to the groups identified from proteomic data. Of the 271 reproducible spots on our 2DE gels, we selected 15 to distinguish the human liver cytosolic clusters. Using MALDI-TOF peptide mass fingerprinting, we identified 12 proteins for the selected spots, including known cancer-associated species.</p><p>Conclusions/Significance</p><p>Our results highlight the importance of hierarchical cluster analysis of proteomic data, and showed concordance between results of biochemical and proteomic approaches. Grouping of the human liver samples and/or patients into differing clusters may provide insights into possible molecular mechanism of drug metabolism and creates a rationale for personalized treatment.</p></div

The number of protein spots after manual processing of 19 2DE gel images for the human liver cytosol using the proprietary GelEditor software.

<p>Gels belonging to cluster 1 are shown in white columns. Gels belonging to cluster 2 are shown in gray columns. Gel which removed from all subsequent data processing is shown in the shaded column.</p

Hierarchical clustering applied to 2DE gel images of the human liver cytosol (a) and cytochrome P450 enzymes activities (mol/min/nmol P450) of the human liver microsomes (b).

<p>Two major clusters can be seen.</p