کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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5371490 | 1503955 | 2010 | 7 صفحه PDF | دانلود رایگان |

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,' Ï, 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 Ï. 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 Ï. 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 AbstractResearch HighlightsâºResearch Highlights âºStatistical methods for plasma thermogram analysis. âºCharacterization of disease states. âºAnalysis of simulated plasma thermograms. âºClassification of Healthy and SLE (lupus) thermograms.
Journal: Biophysical Chemistry - Volume 152, Issues 1â3, November 2010, Pages 184-190