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Density-based separation in multiphase systems provides a simple method to identify sickle cell disease.

ABSTRACT: Although effective low-cost interventions exist, child mortality attributable to sickle cell disease (SCD) remains high in low-resource areas due, in large part, to the lack of accessible diagnostic methods. The presence of dense (? > 1.120 g/cm(3)) cells is characteristic of SCD. The fluid, self-assembling step-gradients in density created by aqueous multiphase systems (AMPSs) identifies SCD by detecting dense cells. AMPSs separate different forms of red blood cells by density in a microhematocrit centrifuge and provide a visual means to distinguish individuals with SCD from those with normal hemoglobin or with nondisease, sickle-cell trait in under 12 min. Visual evaluation of a simple two-phase system identified the two main subclasses of SCD [homozygous (Hb SS) and heterozygous (Hb SC)] with a sensitivity of 90% (73-98%) and a specificity of 97% (86-100%). A three-phase system identified these two types of SCD with a sensitivity of 91% (78-98%) and a specificity of 88% (74-98%). This system could also distinguish between Hb SS and Hb SC. To the authors' knowledge, this test demonstrates the first separation of cells by density with AMPSs, and the usefulness of AMPSs in point-of-care diagnostic hematology.

SUBMITTER: Kumar AA

PROVIDER: S-EPMC4205650 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Density-based separation in multiphase systems provides a simple method to identify sickle cell disease.

Kumar Ashok A AA Patton Matthew R MR Hennek Jonathan W JW Lee Si Yi Ryan SY D'Alesio-Spina Gaetana G Yang Xiaoxi X Kanter Julie J Shevkoplyas Sergey S SS Brugnara Carlo C Whitesides George M GM

Proceedings of the National Academy of Sciences of the United States of America 20140902 41

Although effective low-cost interventions exist, child mortality attributable to sickle cell disease (SCD) remains high in low-resource areas due, in large part, to the lack of accessible diagnostic methods. The presence of dense (ρ > 1.120 g/cm(3)) cells is characteristic of SCD. The fluid, self-assembling step-gradients in density created by aqueous multiphase systems (AMPSs) identifies SCD by detecting dense cells. AMPSs separate different forms of red blood cells by density in a microhematoc ...[more]

PMID: 25197072

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Project description:BackgroundPhysiologic signals, such as cardiac interbeat intervals, exhibit complex fluctuations. However, capturing important dynamical properties, including nonstationarities may not be feasible from conventional time series graphical representations.MethodsWe introduce a simple-to-implement visualisation method, termed dynamical density delay mapping ("D3-Map" technique) that provides an animated representation of a system's dynamics. The method is based on a generalization of conventional two-dimensional (2D) Poincaré plots, which are scatter plots where each data point, x(n), in a time series is plotted against the adjacent one, x(n?+?1). First, we divide the original time series, x(n) (n?=?1,…, N), into a sequence of segments (windows). Next, for each segment, a three-dimensional (3D) Poincaré surface plot of x(n), x(n?+?1), h[x(n),x(n?+?1)] is generated, in which the third dimension, h, represents the relative frequency of occurrence of each (x(n),x(n?+?1)) point. This 3D Poincaré surface is then chromatised by mapping the relative frequency h values onto a colour scheme. We also generate a colourised 2D contour plot from each time series segment using the same colourmap scheme as for the 3D Poincaré surface. Finally, the original time series graph, the colourised 3D Poincaré surface plot, and its projection as a colourised 2D contour map for each segment, are animated to create the full "D3-Map."ResultsWe first exemplify the D3-Map method using the cardiac interbeat interval time series from a healthy subject during sleeping hours. The animations uncover complex dynamical changes, such as transitions between states, and the relative amount of time the system spends in each state. We also illustrate the utility of the method in detecting hidden temporal patterns in the heart rate dynamics of a patient with atrial fibrillation. The videos, as well as the source code, are made publicly available.ConclusionsAnimations based on density delay maps provide a new way of visualising dynamical properties of complex systems not apparent in time series graphs or standard Poincaré plot representations. Trainees in a variety of fields may find the animations useful as illustrations of fundamental but challenging concepts, such as nonstationarity and multistability. For investigators, the method may facilitate data exploration.

Dataset Information

Density-based separation in multiphase systems provides a simple method to identify sickle cell disease.

Publications

Density-based separation in multiphase systems provides a simple method to identify sickle cell disease.

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