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Lipid shell-stabilized nanoparticles with a perfluorocarbon gas-core, or nanobubbles, have recently attracted attention as a new contrast agent for molecular ultrasound imaging and image-guided therapy. Due to their small size (∼275 nm diameter) and flexible shell, nanobubbles have been shown to extravasate through hyperpermeable vasculature ( e.g. , in tumors). However, little is known about the dynamics and depth of extravasation of intact, acoustically active nanobubbles. Accordingly, in this work, we developed a microfluidic chip with a lumen and extracellular matrix (ECM) and imaging method that allows real-time imaging and characterization of the extravasation process with high-frequency ultrasound. The microfluidic device has a lumen and is surrounded by an extracellular matrix with tunable porosity. The combination of ultrasound imaging and the microfluidic chip advantageously produces real-time images of the entire length and depth of the matrix. This captures the matrix heterogeneity, offering advantages over other imaging techniques with smaller fields of view. Results from this study show that nanobubbles diffuse through a 1.3 μm pore size (2 mg mL -1 ) collagen I matrix 25× faster with a penetration depth that was 0.19 mm deeper than a 3.7 μm (4 mg mL -1 ) matrix. In the 3.7 μm pore size matrix, nanobubbles diffused 92× faster than large nanobubbles (∼875 nm diameter). Decorrelation time analysis was successfully used to differentiate flowing and extra-luminally diffusing nanobubbles. In this work, we show for the first time that combination of an ultrasound-capable microfluidic chip and real-time imaging provided valuable insight into spatiotemporal nanoparticle movement through a heterogeneous extracellular matrix. This work could help accurately predict parameters ( e.g. , injection dosage) that improve translation of nanoparticles from in vitro to in vivo environments.

Single cells have unique biophysical signatures that can rapidly change during various disease states. For instance, cellular density is an inherent property differing between cell types. Characterizing changes in fundamental density properties down to the single-cell level can reveal sub-populations in pathological states. Here, we have developed a microfluidic, magnetic levitation-based assay (MagDense) that detects minute density differences of individual red blood cells (RBCs) down to 0.0001 g mL -1 resolution. This assay fractionates RBCs based on their density profiles in a non-ionic paramagnetic medium flowing in a capillary microchannel placed between magnets with same poles facing each other. Based on precisely measured levitation height and density of individual RBCs at their specific equilibrium state, we demonstrated that MagDense can accurately analyze the density of sickle hemoglobin (HbS)-containing RBCs and normal hemoglobin (HbA)-containing RBCs. In addition, the precise density and cell size measurements at the single cell level showed three different sub-populations of RBCs in blood samples from individuals with homozygous sickle cell disease receiving blood transfusions; where less dense, HbA-containing RBCs levitated higher, while the denser, HbS-containing RBCs levitated lower. We compared the mean RBC densities of sickle cell disease subjects with healthy controls and found distinctly separated bands of RBC density for each group denoting the likely range of cell densities seen in the blood samples. The high resolution of our method enabled measurement of deviation from the mean RBC density. Moreover, we introduced a new term as a measure of density dispersion, "RBC levitational density width, RLDW". Mean RBC density in sickle cell disease associated with hemoglobin from complete blood count ( p = 0.032, linear regression) and RLDW associated with absolute reticulocyte count (ARC) and RBC distribution width (RDW) from complete blood count ( p = 0.002 for ARC and p = 003 for RDW, linear regression). Our magnetic levitation-based assay enables rapid, accurate, density-based imaging, profiling and label-free monitoring of single RBCs. Our approach can be broadly applicable to investigate blood cell disorders and the effects of emerging pharmacological and curative therapies in patient outcomes.

Anemia affects over 25% of the world's population with the heaviest burden borne by women and children. Genetic hemoglobin (Hb) variants, such as sickle cell disease, are among the major causes of anemia. Anemia and Hb variant are pathologically interrelated and have an overlapping geographical distribution. We present the first point-of-care (POC) platform to perform both anemia detection and Hb variant identification, using a single paper-based electrophoresis test. Feasibility of this new integrated diagnostic approach is demonstrated via testing individuals with anemia and/or sickle cell disease. Hemoglobin level determination is performed by an artificial neural network (ANN) based machine learning algorithm, which achieves a mean absolute error of 0.55 g dL -1 and a bias of -0.10 g dL -1 against the gold standard (95% limits of agreement: 1.5 g dL -1 ) from Bland-Altman analysis on the test set. Resultant anemia detection is achieved with 100% sensitivity and 92.3% specificity. With the same tests, subjects with sickle cell disease were identified with 100% sensitivity and specificity. Overall, the presented platform enabled, for the first time, integrated anemia detection and hemoglobin variant identification using a single point-of-care test.

Anemia, characterized by low blood hemoglobin level, affects about 25% of the world's population with the heaviest burden borne by women and children. Anemia leads to impaired cognitive development in children, as well as high morbidity and early mortality among sufferers. Anemia can be caused by nutritional deficiencies, oncologic treatments and diseases, and infections such as malaria, as well as inherited hemoglobin or red cell disorders. Effective treatments are available for anemia upon early detection and the treatment method is highly dependent on the cause of anemia. There is a need for point-of-care (POC) screening, early diagnosis, and monitoring of anemia, which is currently not widely accessible due to technical challenges and cost, especially in low- and middle-income countries where anemia is most prevalent. This review first introduces the evolution of anemia detection methods followed by their implementation in current commercially available POC anemia diagnostic devices. Then, emerging POC anemia detection technologies leveraging new methods are reviewed. Finally, we highlight the future trends of integrating anemia detection with the diagnosis of relevant underlying disorders to accurately identify specific root causes and to facilitate personalized treatment and care.