Portland Bioscience Inc.

Portland, OR, United States

Portland Bioscience Inc.

Portland, OR, United States

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Fish D.J.,Portland Bioscience Inc. | Fish D.J.,Louisville Bioscience Inc. | Brewood G.P.,Portland Bioscience Inc. | Brewood G.P.,Louisville Bioscience Inc. | And 6 more authors.
Biophysical Chemistry | Year: 2010

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. © 2010 Elsevier B.V.


Williams L.,University of Utah | Blair S.,University of Utah | Chagovetz A.,University of Utah | Fish D.J.,Portland Bioscience Inc. | Benight A.S.,Portland Bioscience Inc.
Analytical Biochemistry | Year: 2011

Under equilibrium conditions, there are two regimes of target capture on a surface - target limited and probe limited. In the probe limited regime, the melting curve from multiplex target dissociation from the surface exhibits a single transition due to a reverse displacement mechanism of the low affinity species. The melting curve cannot be used in analytical methods to resolve heteroduplexes; only with the simplex system can proper thermodynamics be obtained. © 2010 Elsevier Inc. All rights reserved.


PubMed | Portland Bioscience Inc.
Type: Journal Article | Journal: Biophysical chemistry | Year: 2010

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, , 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 . 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 . 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.

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