We present a comprehensive theoretical-experimental framework for quantitative, high-throughput study of cell biomechanics. An improved electrodeformation method has been developed by combing dielectrophoresis and amplitude shift keying, a form of amplitude modulation. This method offers a potential to fully control the magnitude and rate of deformation in cell membranes. In healthy human red blood cells, nonlinear viscoelasticity of cell membranes is obtained through variable amplitude load testing. A mathematical model to predict cellular deformations is validated using the experimental results of healthy human red blood cells subjected to various types of loading. These results demonstrate new capabilities of the electrodeformation technique and the validated mathematical model to explore the effects of different loading configurations on the cellular mechanical behavior. This gives it more advantages over existing methods and can be further developed to study the effects of strain rate and loading waveform on the mechanical properties of biological cells in health and disease.

This article presents the development and testing of a low‐cost (<$60), portable, electrical impedance‐based microflow cytometer for single‐cell analysis under a controlled oxygen microenvironment. The system is based on an AD5933 impedance analyzer chip, a microfluidic chip, and an Arduino microcontroller operated by a custom Android application. A representative case study on human red blood cells (RBCs) affected by sickle cell disease is conducted to demonstrate the capability of the cytometry system. Impedance values of sickle blood samples exhibit remarkable deviations from the common reference line obtained from two normal blood samples. Such deviation is quantified by a conformity score, which allows for the measurement of intrapatient and interpatient variations of sickle cell disease. A low conformity score under oxygenated conditions or drastically different conformity scores between oxygenated and deoxygenated conditions can be used to differentiate a sickle blood sample from normal. Furthermore, an equivalent circuit model of a suspended biological cell is used to interpret the electrical impedance of single flowing RBCs. In response to hypoxia treatment, all samples, regardless of disease state, exhibit significant changes in at least one single‐cell electrical property, that is, cytoplasmic resistance and membrane capacitance. The overall response to hypoxia is less in normal cells than those affected by sickle cell disease, where the change in membrane capacitance varies from −23% to seven times as compared with −17% in normal cells. The results reported in this article suggest that the developed method of testing demonstrates the potential application for a low‐cost screening technique for sickle cell disease and other diseases in the field and low‐resource settings. The developed system and methodology can be extended to analyze cellular response to hypoxia in other cell types.

Dielectrophoresis (DEP) has been demonstrated as an effective mechanism for cell sorting in microfluidic settings. Many existing methods utilize sophisticated microfluidic designs that require complicated fabrication process and operations. In this paper, we present a microfluidics-based cell sorter that is capable of sorting microparticles continuously in a simple straight channel, thus facilitating easier fabrication and operation. An array of indium-tin oxide (ITO) electrodes are embedded on the bottom surface of the straight channel to generate a DEP force field. This force results in deviation of the particles with different dielectric properties from their paths that are hydrodynamically focused in the channel. Particle trajectories are predicted by numerical simulation at different flow rates and field strengths using COMSOL. Separation of red blood cells from polystyrene beads is demonstrated and numerical prediction is validated experimentally. High separation efficiency for the two particle types is confirmed by counting the concentrations of particles collected at the respective collection outlet.

Sickle Cell Disease (SCD) is a genetic condition caused by a mutated hemoglobin molecule (HbS) found in red blood cells (RBCs). HbS polymerizes in low oxygen environments and contribute to painful vaso-occlusion in patients. Laboratory diagnosis of SCD is typically made by detection of the presence of sickle cells by peripheral blood smear, and presence of HbS by electrophoresis and high-performance liquid chromatography. Recently, flow cytometry technique in companion with sickling assays has demonstrated the capability in quantitative measurements of sickle cells at single-cell level, using software algorithm for cell-imaging analysis (Van Beers et. al. American Journal of Hematology 2014), and electrical impedance (Liu et. al. Sensors and Actuators B: Chemical 2018). Here, we show a portable, cost-efficient electrical impedance-based sensor and its capability to be used in conjunction with microfluidics-based sickling assay for microflow cytometry of sickle cells. The impedance microflow cytometer is based on a commercially available integrated circuit (IC), the AD5933. Using a microcontroller and additional circuitry on a custom designed printed circuit board, we are able to produce sinusoidal signals of up to 100kHz in frequency and sample up to 200 data points per second, at a cost under $60 in materials to create. The impedance measurement range is optimized to work in companion with microfluidic chips in general. In order to measure sickle cells, the impedance microflow cytometer is used in companion with our unique Polydimethylsiloxane (PDMS) microfluidic cell sickling assay (Du et. al. PNAS 2014). Cells are suspended in phosphate buffered saline (PBS) medium and move in the microchannel using a pressure driven flow. Impedance measurement is achieved using two Ti/Au electrodes embedded in the microchannel as cells flow past the electrodes. Data is captured and made available for post processing using a customized MATLAB script. RBCs from healthy donors and SCD patients were used to demonstrate the capability of the developed system. The results showed that our system can separate between normal RBCs and sickle cells, as well as between sickled and unsickled cells. The performance in detection of sickle cells is comparable to a commercial impedance analyzer. This proof-of-concept design aims to minimize the physical space needed for cytometry as well as bring affordable and reliable cytometry results within its given limitations. Figure Disclosures Alvarez: Forma Therapeutics: Consultancy; Novartis: Consultancy.

Individuals who have suffered neurotrauma like a stroke or brachial plexus injury often experience reduced limb functionality. Soft robotic exoskeletons have been successful in assisting rehabilitative treatment and improving activities of daily life but restoring dexterity for tasks such as playing musical instruments has proven challenging. This research presents a soft robotic hand exoskeleton coupled with machine learning algorithms to aid in relearning how to play the piano by ‘feeling’ the difference between correct and incorrect versions of the same song. The exoskeleton features piezoresistive sensor arrays with 16 taxels integrated into each fingertip. The hand exoskeleton was created as a single unit, with polyvinyl acid (PVA) used as a stent and later dissolved to construct the internal pressure chambers for the five individually actuated digits. Ten variations of a song were produced, one that was correct and nine containing rhythmic errors. To classify these song variations, Random Forest (RF), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) algorithms were trained with data from the 80 taxels combined from the tactile sensors in the fingertips. Feeling the differences between correct and incorrect versions of the song was done with the exoskeleton independently and while the exoskeleton was worn by a person. Results demonstrated that the ANN algorithm had the highest classification accuracy of 97.13% ± 2.00% with the human subject and 94.60% ± 1.26% without. These findings highlight the potential of the smart exoskeleton to aid disabled individuals in relearning dexterous tasks like playing musical instruments.