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Biophys Chem. 2010 Nov;152(1-3):184-90. doi: 10.1016/j.bpc.2010.09.007. Epub 2010 Sep 29.

Statistical analysis of plasma thermograms measured by differential scanning calorimetry.

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  • 1Portland Bioscience, Inc., Portland, OR, United States. djf@pdxbio.com

Abstract

Melting curves of human plasma measured by differential scanning calorimetry (DSC), known as thermograms, have the potential to markedly impact diagnosis of human diseases. A general statistical methodology is developed to analyze and classify DSC thermograms to analyze and classify thermograms. Analysis of an acquired thermogram involves comparison with a database of empirical reference thermograms from clinically characterized diseases. Two parameters, a distance metric, P, and correlation coefficient, r, are combined to produce a 'similarity metric,' ρ, which can be used to classify unknown thermograms into pre-characterized categories. Simulated thermograms known to lie within or fall outside of the 90% quantile range around a median reference are also analyzed. Results verify the utility of the methods and establish the apparent dynamic range of the metric ρ. Methods are then applied to data obtained from a collection of plasma samples from patients clinically diagnosed with SLE (lupus). High correspondence is found between curve shapes and values of the metric ρ. In a final application, an elementary classification rule is implemented to successfully analyze and classify unlabeled thermograms. These methods constitute a set of powerful yet easy to implement tools for quantitative classification, analysis and interpretation of DSC plasma melting curves.

Copyright © 2010 Elsevier B.V. All rights reserved.

PMID:
20961680
[PubMed - indexed for MEDLINE]
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