Article ID Journal Published Year Pages File Type
5371490 Biophysical Chemistry 2010 7 Pages PDF
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.

Graphical AbstractDownload full-size imageResearch Highlights►Research Highlights ►Statistical methods for plasma thermogram analysis. ►Characterization of disease states. ►Analysis of simulated plasma thermograms. ►Classification of Healthy and SLE (lupus) thermograms.

Related Topics
Physical Sciences and Engineering Chemistry Physical and Theoretical Chemistry
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