Diagnosis of iron deficiency anemia using density-based fractionation of red blood cells.

Iron deficiency anemia (IDA) is a nutritional disorder that impacts over one billion people worldwide; it may cause permanent cognitive impairment in children, fatigue in adults, and suboptimal outcomes in pregnancy. IDA can be diagnosed by detection of red blood cells (RBCs) that are characteristically small (microcytic) and deficient in hemoglobin (hypochromic), typically by examining the results of a complete blood count performed by a hematology analyzer. These instruments are expensive, not portable, and require trained personnel; they are, therefore, unavailable in many low-resource settings. This paper describes a low-cost and rapid method to diagnose IDA using aqueous multiphase systems (AMPS)-thermodynamically stable mixtures of biocompatible polymers and salt that spontaneously form discrete layers having sharp steps in density. AMPS are preloaded into a microhematocrit tube and used with a drop of blood from a fingerstick. After only two minutes in a low-cost centrifuge, the tests (n = 152) were read by eye with a sensitivity of 84% (72-93%) and a specificity of 78% (68-86%), corresponding to an area under the curve (AUC) of 0.89. The AMPS test outperforms diagnosis by hemoglobin alone (AUC = 0.73) and is comparable to methods used in clinics like reticulocyte hemoglobin concentration (AUC = 0.91). Standard machine learning tools were used to analyze images of the resulting tests captured by a standard desktop scanner to 1) slightly improve diagnosis of IDA-sensitivity of 90% (83-96%) and a specificity of 77% (64-87%), and 2) predict several important red blood cell parameters, such as mean corpuscular hemoglobin concentration. These results suggest that the use of AMPS combined with machine learning provides an approach to developing point-of-care hematology.

Evaluation of a density-based rapid diagnostic test for sickle cell disease in a clinical setting in Zambia.

Although simple and low-cost interventions for sickle cell disease (SCD) exist in many developing countries, child mortality associated with SCD remains high, in part, because of the lack of access to diagnostic tests for SCD. A density-based test using aqueous multiphase systems (SCD-AMPS) is a candidate for a low-cost, point-of-care diagnostic for SCD. In this paper, the field evaluation of SCD-AMPS in a large (n = 505) case-control study in Zambia is described. Of the two variations of the SCD-AMPS used, the best system (SCD-AMPS-2) demonstrated a sensitivity of 86% (82-90%) and a specificity of 60% (53-67%). Subsequent analysis identified potential sources of false positives that include clotting, variation between batches of SCD-AMPS, and shipping conditions. Importantly, SCD-AMPS-2 was 84% (62-94%) sensitive in detecting SCD in children between 6 months and 1 year old. In addition to an evaluation of performance, an assessment of end-user operability was done with health workers in rural clinics in Zambia. These health workers rated the SCD-AMPS tests to be as simple to use as lateral flow tests for malaria and HIV.

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

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 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.

Separation of nanoparticles in aqueous multiphase systems through centrifugation.

This paper demonstrates the use of aqueous multiphase systems (MuPSs) as media for rate-zonal centrifugation to separate nanoparticles of different shapes and sizes. The properties of MuPSs do not change with time or during centrifugation; this stability facilitates sample collection after separation. A three-phase system demonstrates the separation of the reaction products (nanorods, nanospheres, and large particles) of a synthesis of gold nanorods, and enriches the nanorods from 48 to 99% in less than ten minutes using a benchtop centrifuge.

Aqueous multiphase systems of polymers and surfactants provide self-assembling step-gradients in density.

This Communication demonstrates the generation of over 300 phase-separated systems-ranging from two to six phases-from mixtures of aqueous solutions of polymers and surfactants. These aqueous multiphase systems (MuPSs) form self-assembling, thermodynamically stable step-gradients in density using a common solvent, water. The steps in density between phases of a MuPS can be very small (Δρ ≈ 0.001 g/cm(3)), do not change over time, and can be tuned by the addition of co-solutes. We use two sets of similar objects, glass beads and pellets of different formulations of Nylon, to demonstrate the ability of MuPSs to separate mixtures of objects by differences in density. The stable interfaces between phases facilitate the convenient collection of species after separation. These results suggest that the stable, sharp step-gradients in density provided by MuPSs can enable new classes of fractionations and separations based on density.