Sickle cell detection using a smartphone

Smartphone microscopic method for imaging and quantification of microplastics in drinking water

Analysis of microplastics in drinking water is often challenging due to smaller particle size and low particle count. In this study, we used a low cost and an easy to assemble smartphone microscopic system for imaging and quantitating microplastic particles as small as 20 μm. The system consisted of a spherical sapphire ball lens of 4 mm diameter attached to a smartphone camera as a major imaging component. It also involved pre-concentration of the sample using ZnCl2 solution. The spike recovery and limit of detection of the method in filtered distilled and deionized water samples (n = 9) were 55.6% ± 9.7% and 34 particles/L, respectively. Imaging performance of the microscopic system was similar to a commercial bright field microscopic system. The method was further implemented to examine microplastic particles in commercial bottled and jar water samples (n = 20). The particles count in bottled and jar water samples ranged from 0-91 particles/L to 0-130 particles/L, respectively. In both sample types, particles of diverse shape and size were observed. The particles collected from water samples were further confirmed by FTIR spectra (n = 36), which found 97% of the particles tested were made of plastic material. These findings suggested that the smartphone microscopic system can be implemented as a low-cost alternative for preliminary screening of microplastic in drinking water samples. RESEARCH HIGHLIGHTS: Ball lens based smartphone microscopic method was used for microplastic analysis. Particles of diverse shape and size were found in bottle and jar water samples.

A commentary on the development and use of smartphone imaging devices

Current-age smartphones are known for their wide array of functionality and are now being utilized in the field of healthcare and medicine due to their proven capabilities as smartphone imaging devices (SIDs). Recent technical advancements enabled the integration of special add-on lenses with smartphones to transform them into SIDs. With the rising demand for efficient point-of-care (PoC) devices for better diagnostic applications, SIDs will be a one-stop solution. Additionally, portability, user-friendliness and low-cost make it accessible for all even at remote locations. Furthermore, improvements in resolution, magnification and field-of-view (FOV) have attracted the scientific community to use SIDs in various biomedical applications such as disease diagnosis, food quality control and pathogen detection. SIDs can be arranged in various combinational setups by using different illumination sources and optics to achieve suitable contrast and visibility of the specimen under study. This Commentary illustrates the various illumination sources used in SID and also spotlights their design and applications.

Measuring magnetic force field distributions in microfluidic devices: Experimental and numerical approaches

Precisely and accurately determining the magnetic force and its spatial distribution in microfluidic devices is challenging. Typically, magnetic microfluidic devices are designed in a way to both maximize the force within the separation region and to minimize the necessity for knowing such details-such as designing magnetic geometries that create regions of nearly constant magnetic force or that dictate the behavior of the magnetic force to be highly predictable in a specified region. In this work, we present a method to determine the spatial distribution of the magnetic force field in a magnetic microfluidic device by particle tracking magnetophoresis. Polystyrene microparticles were suspended in a paramagnetic fluid, gadolinium, and this suspension was exposed to various magnetic field geometries. Polystyrene particle motion was tracked using a microscope and images processed using Fiji (ImageJ). From a sample with a large spatial distribution of particle tracks, the magnetic force field distribution was calculated. The force field distribution was fitted to nonlinear spatial distribution models. These experimental models are compared to and supported by 3D simulations of the magnetic force field in COMSOL.

Artificial intelligence in sickle disease

Artificial intelligence (AI) is rapidly becoming an established arm in medical sciences and clinical practice in numerous medical fields. Its implications have been rising and are being widely used in research, diagnostics, and treatment options for many pathologies, including sickle cell disease (SCD). AI has started new ways to improve risk stratification and diagnosing SCD complications early, allowing rapid intervention and reallocation of resources to high-risk patients. We reviewed the literature for established and new AI applications that may enhance management of SCD through advancements in diagnosing SCD and its complications, risk stratification, and the effect of AI in establishing an individualized approach in managing SCD patients in the future. Aim: to review the benefits and drawbacks of resources utilizing AI in clinical practice for improving the management for SCD cases.

Multi-omics analysis of magnetically levitated plasma biomolecules

We recently discovered that superparamagnetic iron oxide nanoparticles (SPIONs) can levitate plasma biomolecules in the magnetic levitation (MagLev) system and cause formation of ellipsoidal biomolecular bands. To better understand the composition of the levitated biomolecules in various bands, we comprehensively characterized them by multi-omics analyses. To probe whether the biomolecular composition of the levitated ellipsoidal bands correlates with the health of plasma donors, we used plasma from individuals who had various types of multiple sclerosis (MS), as a model disease with significant clinical importance. Our findings reveal that, while the composition of proteins does not show much variability, there are significant differences in the lipidome and metabolome profiles of each magnetically levitated ellipsoidal band. By comparing the lipidome and metabolome compositions of various plasma samples, we found that the levitated biomolecular ellipsoidal bands do contain information on the health status of the plasma donors. More specifically, we demonstrate that there are particular lipids and metabolites in various layers of each specific plasma pattern that significantly contribute to the discrimination of different MS subtypes, i.e., relapsing-remitting MS (RRMS), secondary-progressive MS (SPMS), and primary-progressive MS (PPMS). These findings will pave the way for utilization of MagLev of biomolecules in biomarker discovery for identification of diseases and discrimination of their subtypes.

The Unique Magnetic Signature of Sickle Red Blood Cells: A Comparison Between the Red Blood Cells of Transfused and Non-Transfused Sickle Cell Disease Patients and Healthy Donors

Sickle cell disease (SCD) is an inherited blood disorder that affects millions of people worldwide, especially in low-resource regions of the world, where a rapid and affordable test to properly diagnose the disease would be highly valued. Magnetophoresis is a technique that could simultaneously analyze, quantify, and potentially separate the patient's sickle red blood cells (RBCs) from healthy RBCs, but the magnetic characteristics of sickle RBCs have yet to be reported. In this work, we present the single cell magnetic characterization of RBCs obtained from SCD patients. Sufficient single cells are analyzed from patient samples undergoing transfusion therapy and not yet having transfusion therapy (TP and NTP, respectively), such that means and distributions of these single RBC mobilities are created in the form of histograms which facilitated comparison to RBCs from healthy donors (HD). The magnetic characterization is obtained using a technique known as Cell Tracking Velocimetry (CTV) that quantitatively characterizes the RBC response to magnetic and gravitational fields. The magnetic properties of RBCs containing oxygenated, deoxygenated hemoglobin (Hb) and methemoglobin (oxyHb-RBCs, deoxyHb-RBCs, and metHb-RBCs) are further determined. The NTP samples reported the highest magnetic character, especially when compared to oxyHb-RBCs from HD, which implies impaired oxygen binding capabilities. Also, the oxygen-Hb equilibrium curves are obtained to estimate the magnetic character of the cells under intermediate oxygen levels. Our results confirm higher magnetic moment of SCD blood (NTP) under intermediate oxygen levels. These data demonstrate the potential feasibility of magnetophoresis to identify, quantify and separate sickle RBCs from healthy RBCs.

Advanced density-based methods for the characterization of materials, binding events, and kinetics

Investigations of the densities of chemicals and materials bring valuable insights into the fundamental understanding of matter and processes. Recently, advanced density-based methods have been developed with wide measurement ranges (i.e. 0-23 g cm^-3), high resolutions (i.e. 10^-6 g cm^-3), compatibility with different types of samples and the requirement of extremely low volumes of sample (as low as a single cell). Certain methods, such as magnetic levitation, are inexpensive, portable and user-friendly. Advanced density-based methods are, therefore, beneficially used to obtain absolute density values, composition of mixtures, characteristics of binding events, and kinetics of chemical and biological processes. Herein, the principles and applications of magnetic levitation, acoustic levitation, electrodynamic balance, aqueous multiphase systems, and suspended microchannel resonators for materials science are discussed.

Metasurfaces for Sensing Applications: Gas, Bio and Chemical

Performance of photonic devices critically depends upon their efficiency on controlling the flow of light therein. In the recent past, the implementation of plasmonics, two-dimensional (2D) materials and metamaterials for enhanced light-matter interaction (through concepts such as sub-wavelength light confinement and dynamic wavefront shape manipulation) led to diverse applications belonging to spectroscopy, imaging and optical sensing etc. While 2D materials such as graphene, MoS₂ etc., are still being explored in optical sensing in last few years, the application of plasmonics and metamaterials is limited owing to the involvement of noble metals having a constant electron density. The capability of competently controlling the electron density of noble metals is very limited. Further, due to absorption characteristics of metals, the plasmonic and metamaterial devices suffer from large optical loss. Hence, the photonic devices (sensors, in particular) require that an efficient dynamic control of light at nanoscale through field (electric or optical) variation using substitute low-loss materials. One such option may be plasmonic metasurfaces. Metasurfaces are arrays of optical antenna-like anisotropic structures (sub-wavelength size), which are designated to control the amplitude and phase of reflected, scattered and transmitted components of incident light radiation. The present review put forth recent development on metamaterial and metastructure-based various sensors.

Deep Learning-Enabled Technologies for Bioimage Analysis

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.

Improving Pain Assessment using Vital Signs and Pain Medication for patients with Sickle Cell Disease: Retrospective Study (Preprint)

Background

Sickle cell disease (SCD) is the most common inherited blood disorder affecting millions of people worldwide. Most patients with SCD experience repeated, unpredictable episodes of severe pain. These pain episodes are the leading cause of emergency department visits among patients with SCD and may last for several weeks. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting a patient’s pain intensity level.

Objective

This study aims to learn deep feature representations of subjective pain trajectories using objective physiological signals collected from electronic health records.

Methods

This study used electronic health record data collected from 496 Duke University Medical Center participants over 5 consecutive years. Each record contained measures for 6 vital signs and the patient’s self-reported pain score, with an ordinal range from 0 (no pain) to 10 (severe and unbearable pain). We also extracted 3 features related to medication: medication type, medication status (given or applied, or missed or removed or due), and total medication dosage (mg/mL). We used variational autoencoders for representation learning and designed machine learning classification algorithms to build pain prediction models. We evaluated our results using an accuracy and confusion matrix and visualized the qualitative data representations.

Results

We designed a classification model using raw data and deep representational learning to predict subjective pain scores with average accuracies of 82.8%, 70.6%, 49.3%, and 47.4% for 2-point, 4-point, 6-point, and 11-point pain ratings, respectively. We observed that random forest classification models trained on deep represented features outperformed models trained on unrepresented data for all pain rating scales. We observed that at varying Likert scales, our models performed better when provided with medication data along with vital signs data. We visualized the data representations to understand the underlying latent representations, indicating neighboring representations for similar pain scores with a higher resolution of pain ratings.

Conclusions

Our results demonstrate that medication information (the type of medication, total medication dosage, and whether the medication was given or missed) can significantly improve subjective pain prediction modeling compared with modeling with only vital signs. This study shows promise in data-driven estimated pain scores that will help clinicians with additional information about the patient’s condition, in addition to the patient’s self-reported pain scores.

An affordable, handheld multimodal microscopic system with onboard cell morphology and counting features on a mobile device

A simple yet effective, handheld and flexible bright-field and fluorescence microscopic platform on a smartphone with varying optical magnifications is reported for morphological analysis and onboard cell counting features.

Point-of-care microchip electrophoresis for integrated anemia and hemoglobin variant testing

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.

Design and Adoption of Low-Cost Point-of-Care Diagnostic Devices: Syrian Case

Civil wars produce immense humanitarian crises, causing millions of individuals to seek refuge in other countries. The rate of disease prevalence has inclined among the refugees, increasing the cost of healthcare. Complex medical conditions and high numbers of patients at healthcare centers overwhelm the healthcare system and delay diagnosis and treatment. Point-of-care (PoC) testing can provide efficient solutions to high equipment cost, late diagnosis, and low accessibility of healthcare services. However, the development of PoC devices in developing countries is challenged by several barriers. Such PoC devices may not be adopted due to prejudices about new technologies and the need for special training to use some of these devices. Here, we investigated the concerns of end users regarding PoC devices by surveying healthcare workers and doctors. The tendency to adopt PoC device changes is based on demographic factors such as work sector, education, and technology experience. The most apparent concern about PoC devices was issues regarding low accuracy, according to the surveyed clinicians.

HologLev: A Hybrid Magnetic Levitation Platform Integrated with Lensless Holographic Microscopy for Density-Based Cell Analysis

In clinical practice, a variety of diagnostic applications require the identification of target cells. Density has been used as a physical marker to distinguish cell populations since metabolic activities could alter the cell densities. Magnetic levitation offers great promise for separating cells at the single cell level within heterogeneous populations with respect to cell densities. Traditional magnetic levitation platforms need bulky and precise optical microscopes to visualize levitated cells. Moreover, the evaluation process of cell densities is cumbersome, which also requires trained personnel for operation. In this work, we introduce a device (HologLev) as a fusion of the magnetic levitation principle and lensless digital inline holographic microscopy (LDIHM). LDIHM provides ease of use by getting rid of bulky and expensive optics. By placing an imaging sensor just beneath the microcapillary channel without any lenses, recorded holograms are processed for determining cell densities through a fully automated digital image processing scheme. The device costs less than $100 and has a compact design that can fit into a pocket. We perform viability tests on the device by levitating three different cell lines (MDA-MB-231, U937, D1 ORL UVA) and comparing them against their dead correspondents. We also tested the differentiation of mouse osteoblastic (7F2) cells by monitoring characteristic variations in their density. Last, the response of MDA-MB-231 cancer cells to a chemotherapy drug was demonstrated in our platform. HologLev provides cost-effective, label-free, fully automated cell analysis in a compact design that could be highly desirable for laboratory and point-of-care testing applications.

3D printed imaging platform for portable cell counting

Despite having widespread application in the biomedical sciences, flow cytometers have several limitations that prevent their application to point-of-care (POC) diagnostics in resource-limited environments. 3D printing provides a cost-effective approach to improve the accessibility of POC devices in resource-limited environments. Towards this goal, we introduce a 3D-printed imaging platform (3DPIP) capable of accurately counting particles and perform fluorescence microscopy. In our 3DPIP, captured microscopic images of particle flow are processed on a custom developed particle counter code to provide a particle count. This prototype uses a machine vision-based algorithm to identify particles from captured flow images and is flexible enough to allow for labeled and label-free particle counting. Additionally, the particle counter code returns particle coordinates with respect to time which can further be used to perform particle image velocimetry. These results can help estimate forces acting on particles, and identify and sort different types of cells/particles. We evaluated the performance of this prototype by counting 10 μm polystyrene particles diluted in deionized water at different concentrations and comparing the results with a commercial Beckman-Coulter Z2 particle counter. The 3DPIP can count particle concentrations down to ∼100 particles per mL with a standard deviation of ±20 particles, which is comparable to the results obtained on a commercial particle counter. Our platform produces accurate results at flow rates up to 9 mL h-1 for concentrations below 1000 particle per mL, while 5 mL h-1 produces accurate results above this concentration limit. Aside from performing flow-through experiments, our instrument is capable of performing static experiments that are comparable to a plate reader. In this configuration, our instrument is able to count between 10 and 250 cells per image, depending on the prepared concentration of bacteria samples (Citrobacter freundii; ATCC 8090). Overall, this platform represents a first step towards the development of an affordable fully 3D printable imaging flow cytometry instrument for use in resource-limited clinical environments.

Multiparametric biophysical profiling of red blood cells in malaria infection

Biophysical separation promises label-free, less-invasive methods to manipulate the diverse properties of live cells, such as density, magnetic susceptibility, and morphological characteristics. However, some cellular changes are so minute that they are undetectable by current methods. We developed a multiparametric cell-separation approach to profile cells with simultaneously changing density and magnetic susceptibility. We demonstrated this approach with the natural biophysical phenomenon of Plasmodium falciparum infection, which modifies its host erythrocyte by simultaneously decreasing density and increasing magnetic susceptibility. Current approaches have used these properties separately to isolate later-stage infected cells, but not in combination. We present biophysical separation of infected erythrocytes by balancing gravitational and magnetic forces to differentiate infected cell stages, including early stages for the first time, using magnetic levitation. We quantified height distributions of erythrocyte populations-27 ring-stage synchronized samples and 35 uninfected controls-and quantified their unique biophysical signatures. This platform can thus enable multidimensional biophysical measurements on unique cell types.

Techniques for the Detection of Sickle Cell Disease: A Review

Sickle cell disease (SCD) is a widespread disease caused by a mutation in the beta-globin gene that leads to the production of abnormal hemoglobin called hemoglobin S. The inheritance of the mutation could be homozygous or heterozygous combined with another hemoglobin mutation. SCD can be characterized by the presence of dense, sickled cells that causes hemolysis of blood cells, anemia, painful episodes, organ damage, and in some cases death. Early detection of SCD can help to reduce the mortality and manage the disease effectively. Therefore, different techniques have been developed to detect the sickle cell disease and the carrier states with high sensitivity and specificity. These techniques can be screening tests such as complete blood count, peripheral blood smears, and sickling test; confirmatory tests such as hemoglobin separation techniques; and genetic tests, which are more expensive and need to be done in centralized labs by highly skilled personnel. However, advanced portable point of care techniques have been developed to provide a low-cost, simple, and user-friendly device for detecting SCD, for instance coupling solubility tests with portable devices, using smartphone microscopic classifications, image processing techniques, rapid immunoassays, and sensor-based platforms. This review provides an overview of the current and emerging techniques for sickle cell disease detection and highlights the different potential methods that could be applied to help the early diagnosis of SCD.

Cellulose-based micro-fibrous materials imaged with a home-built smartphone microscope

Micro-fibrous materials are one of the highly explored materials and form a major component of composite materials. In resource-limited settings, an affordable and easy to implement method that can characterize such material would be important. In this study, we report on a smartphone microscopic system capable of imaging a sample in transmission mode. As a proof of concept, we implemented the method to image handmade paper samples-cellulosic micro-fibrous material of different thickness. With 1 mm diameter ball lens, individual cellulose fibers, fiber web, and micro-porous regions were resolved in the samples. Imaging performance of the microscopic system was also compared with a commercial bright field microscope. For thin samples, we found the image quality comparable to commercial system. Also, the diameter of cellulose fiber measured from both methods was found to be similar. We also used the system to image surfaces of a three ply surgical facemask. Finally, we explored the application of the system in the study of chemical induced fiber damage. This study suggested that the smartphone microscope system can be an affordable alternative in imaging thin micro-fibrous material in resource limited setting.

Hemp-Based Microfluidics

Hemp is a sustainable, recyclable, and high-yield annual crop that can be used to produce textiles, plastics, composites, concrete, fibers, biofuels, bionutrients, and paper. The integration of microfluidic paper-based analytical devices (µPADs) with hemp paper can improve the environmental friendliness and high-throughputness of µPADs. However, there is a lack of sufficient scientific studies exploring the functionality, pros, and cons of hemp as a substrate for µPADs. Herein, we used a desktop pen plotter and commercial markers to pattern hydrophobic barriers on hemp paper, in a single step, in order to characterize the ability of markers to form water-resistant patterns on hemp. In addition, since a higher resolution results in densely packed, cost-effective devices with a minimized need for costly reagents, we examined the smallest and thinnest water-resistant patterns plottable on hemp-based papers. Furthermore, the wicking speed and distance of fluids with different viscosities on Whatman No. 1 and hemp papers were compared. Additionally, the wettability of hemp and Whatman grade 1 paper was compared by measuring their contact angles. Besides, the effects of various channel sizes, as well as the number of branches, on the wicking distance of the channeled hemp paper was studied. The governing equations for the wicking distance on channels with laser-cut and hydrophobic side boundaries are presented and were evaluated with our experimental data, elucidating the applicability of the modified Washburn equation for modeling the wicking distance of fluids on hemp paper-based microfluidic devices. Finally, we validated hemp paper as a substrate for the detection and analysis of the potassium concentration in artificial urine.

Microfluidics for microalgal biotechnology

Microalgae have expanded their roles as renewable and sustainable feedstocks for biofuel, smart nutrition, biopharmaceutical, cosmeceutical, biosensing, and space technologies. They accumulate valuable biochemical compounds from protein, carbohydrate, and lipid groups, including pigments and carotenoids. Microalgal biomass, which can be adopted for multivalorization under biorefinery settings, allows not only the production of various biofuels but also other value-added biotechnological products. However, state-of-the-art technologies are required to optimize yield, quality, and the economical aspects of both upstream and downstream processes. As such, the need to use microfluidic-based devices for both fundamental research and industrial applications of microalgae, arises due to their microscale sizes and dilute cultures. Microfluidics-based devices are superior to their competitors through their ability to perform multiple functions such as sorting and analyzing small amounts of samples (nanoliter to picoliter) with higher sensitivities. Here, we review emerging applications of microfluidic technologies on microalgal processes in cell sorting, cultivation, harvesting, and applications in biofuels, biosensing, drug delivery, and nutrition.

3D-printed microneedles in biomedical applications

Conventional needle technologies can be advanced with emerging nano- and micro-fabrication methods to fabricate microneedles. Nano-/micro-fabricated microneedles seek to mitigate penetration pain and tissue damage, as well as providing accurately controlled robust channels for administrating bioagents and collecting body fluids. Here, design and 3D printing strategies of microneedles are discussed with emerging applications in biomedical devices and healthcare technologies. 3D printing offers customization, cost-efficiency, a rapid turnaround time between design iterations, and enhanced accessibility. Increasing the printing resolution, the accuracy of the features, and the accessibility of low-cost raw printing materials have empowered 3D printing to be utilized for the fabrication of microneedle platforms. The development of 3D-printed microneedles has enabled the evolution of pain-free controlled release drug delivery systems, devices for extracting fluids from the cutaneous tissue, biosignal acquisition, and point-of-care diagnostic devices in personalized medicine.

Increasing the packing density of assays in paper-based microfluidic devices

Paper-based devices have a wide range of applications in point-of-care diagnostics, environmental analysis, and food monitoring. Paper-based devices can be deployed to resource-limited countries and remote settings in developed countries. Paper-based point-of-care devices can provide access to diagnostic assays without significant user training to perform the tests accurately and timely. The market penetration of paper-based assays requires decreased device fabrication costs, including larger packing density of assays (i.e., closely packed features) and minimization of assay reagents. In this review, we discuss fabrication methods that allow for increasing packing density and generating closely packed features in paper-based devices. To ensure that the paper-based device is low-cost, advanced fabrication methods have been developed for the mass production of closely packed assays. These emerging methods will enable minimizing the volume of required samples (e.g., liquid biopsies) and reagents in paper-based microfluidic devices.

Scaffold-free biofabrication of adipocyte structures with magnetic levitation

Tissue engineering research aims to repair the form and/or function of impaired tissues. Tissue engineering studies mostly rely on scaffold-based techniques. However, these techniques have certain challenges, such as the selection of proper scaffold material, including mechanical properties, sterilization, and fabrication processes. As an alternative, we propose a novel scaffold-free adipose tissue biofabrication technique based on magnetic levitation. In this study, a label-free magnetic levitation technique was used to form three-dimensional (3D) scaffold-free adipocyte structures with various fabrication strategies in a microcapillary-based setup. Adipogenic-differentiated 7F2 cells and growth D1 ORL UVA stem cells were used as model cells. The morphological properties of the 3D structures of single and cocultured cells were analyzed. The developed procedure leads to the formation of different patterns of single and cocultured adipocytes without a scaffold. Our results indicated that adipocytes formed loose structures while growth cells were tightly packed during 3D culture in the magnetic levitation platform. This system has potential for ex vivo modeling of adipose tissue for drug testing and transplantation applications for cell therapy in soft tissue damage. Also, it will be possible to extend this technique to other cell and tissue types.

Machine learning-enabled multiplexed microfluidic sensors

High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.

Smartphone-based sickle cell disease detection and monitoring for point-of-care settings

Sickle cell disease (SCD) is a worldwide hematological disorder causing painful episodes, anemia, organ damage, stroke, and even deaths. It is more common in sub-Saharan Africa and other resource-limited countries. Conventional laboratory-based diagnostic methods for SCD are time-consuming, complex, and cannot be performed at point-of-care (POC) and home settings. Optical microscope-based classification and counting demands a significant amount of time, extensive setup, and cost along with the skilled human labor to distinguish the normal red blood cells (RBCs) from sickled cells. There is an unmet need to develop a POC and home-based test to diagnose and monitor SCD and reduce mortality in resource-limited settings. An early-stage and timely diagnosis of SCD can help in the effective management of the disease. In this article, we utilized a smartphone-based image acquisition method for capturing RBC images from the SCD patients in normoxia and hypoxia conditions. A computer algorithm is developed to differentiate RBCs from the patient's blood before and after cell sickling. Using the developed smartphone-based technique, we obtained similar percentage of sickle cells in blood samples as analyzed by conventional method (standard microscope). The developed method of testing demonstrates the potential utility of the smartphone-based test for reducing the overall cost of screening and management for SCD, thus increasing the practicality of smartphone-based screening technique for SCD in low-resource settings. Our setup does not require any special storage requirements. This is the characteristic advantage of our technique as compared to other hemoglobin-based POC diagnostic techniques.

Magnetic Levitation Systems for Disease Diagnostics

Magnetic levitation (MagLev) is a well-documented, robust technique for density measurements and separations. Although the potential of MagLev as an emerging tool in biotechnology has been recently investigated, the practical use of MagLev in diagnosis and disease detection merits further attention. This review highlights the diagnostic capacity of a simple and portable MagLev system and the possibilities and limitations of the MagLev technique for density-based separation, classification, and manipulation of soft matter and biological systems (e.g., cells, proteins), which in turn may pave the way for the discovery of disease-specific biomarkers.

Magnetic Levitation in Chemistry, Materials Science, and Biochemistry

All matter has density. The recorded uses of density to characterize matter date back to as early as ca. 250 BC, when Archimedes was believed to have solved "The Puzzle of The King's Crown" using density.^[1] Today, measurements of density are used to separate and characterize a range of materials (including cells and organisms), and their chemical and/or physical changes in time and space. This Review describes a density-based technique-magnetic levitation (which we call "MagLev" for simplicity)-developed and used to solve problems in the fields of chemistry, materials science, and biochemistry. MagLev has two principal characteristics-simplicity, and applicability to a wide range of materials-that make it useful for a number of applications (for example, characterization of materials, quality control of manufactured plastic parts, self-assembly of objects in 3D, separation of different types of biological cells, and bioanalyses). Its simplicity and breadth of applications also enable its use in low-resource settings (for example-in economically developing regions-in evaluating water/food quality, and in diagnosing disease).

New Methods for Morphological Erythrocytes Classification

Specialists may gauge the severity of sickle cell disease crisis by quantifying the number of abnormal-looking and sickle-shaped erythrocytes in blood smears. State-of-the-art integral geometry-based descriptors for automatic classification of erythrocytes as normal cells, sickle cells or cells with other deformations have achieved excellent results. Unfortunately, they are computationally expensive, requiring powerful desktop computers and a great deal of memory to run. We propose two new integral geometry-based descriptors for the shape of erythrocytes. Like state-of-the-art techniques, the overall sensitivity of our solutions is above 94%. Nevertheless, our descriptors are designed to avoid a great amount of computation in comparison to similar solutions and to present a lower memory footprint. Our descriptors offer a high specificity of normal cells and a high sensitivity of deformed cells, making them a good alternative in clinical applications.

Trends in the Development of Diagnostic Tools for Red Blood Cell-Related Diseases and Anemias

In the recent years, the progress in genetic analysis and next-generation sequencing technologies have opened up exciting landscapes for diagnosis and study of molecular mechanisms, allowing the determination of a particular mutation for individual patients suffering from hereditary red blood cell-related diseases or anemia. However, the huge amount of data obtained makes the interpretation of the results and the identification of the pathogenetic variant responsible for the diseases sometime difficult. Moreover, there is increasing evidence that the same mutation can result in varying cellular properties and different symptoms of the disease. Even for the same patient, the phenotypic expression of the disorder can change over time. Therefore, on top of genetic analysis, there is a further request for functional tests that allow to confirm the pathogenicity of a molecular variant, possibly to predict prognosis and complications (e.g., vaso-occlusive pain crises or other thrombotic events) and, in the best case, to enable personalized theranostics (drug and/or dose) according to the disease state and progression. The mini-review will reflect recent and future directions in the development of diagnostic tools for red blood cell-related diseases and anemias. This includes point of care devices, new incarnations of well-known principles addressing physico-chemical properties, and interactions of red blood cells as well as high-tech screening equipment and mobile laboratories.

Automated screening of sickle cells using a smartphone-based microscope and deep learning

Sickle cell disease (SCD) is a major public health priority throughout much of the world, affecting millions of people. In many regions, particularly those in resource-limited settings, SCD is not consistently diagnosed. In Africa, where the majority of SCD patients reside, more than 50% of the 0.2-0.3 million children born with SCD each year will die from it; many of these deaths are in fact preventable with correct diagnosis and treatment. Here, we present a deep learning framework which can perform automatic screening of sickle cells in blood smears using a smartphone microscope. This framework uses two distinct, complementary deep neural networks. The first neural network enhances and standardizes the blood smear images captured by the smartphone microscope, spatially and spectrally matching the image quality of a laboratory-grade benchtop microscope. The second network acts on the output of the first image enhancement neural network and is used to perform the semantic segmentation between healthy and sickle cells within a blood smear. These segmented images are then used to rapidly determine the SCD diagnosis per patient. We blindly tested this mobile sickle cell detection method using blood smears from 96 unique patients (including 32 SCD patients) that were imaged by our smartphone microscope, and achieved ~98% accuracy, with an area-under-the-curve of 0.998. With its high accuracy, this mobile and cost-effective method has the potential to be used as a screening tool for SCD and other blood cell disorders in resource-limited settings.

Smartphone-based multiplex 30-minute nucleic acid test of live virus from nasal swab extract

Rapid, sensitive and specific detection and reporting of infectious pathogens is important for patient management and epidemic surveillance. We demonstrated a point-of-care system integrated with a smartphone for detecting live virus from nasal swab media, using a panel of equine respiratory infectious diseases as a model system for corresponding human diseases such as COVID-19. Specific nucleic acid sequences of five pathogens were amplified by loop-mediated isothermal amplification on a microfluidic chip and detected at the end of reactions by the smartphone. Pathogen-spiked horse nasal swab samples were correctly diagnosed using our system, with a limit of detection comparable to that of the traditional lab-based test, polymerase chain reaction, with results achieved in ∼30 minutes.

Smartphone epifluorescence microscopy for cellular imaging of fresh tissue in low-resource settings

Disease diagnosis in low-resource settings can be challenging due to the lack of equipment and trained personnel required for histologic analysis. In this paper, we have developed a smartphone-based epifluorescence microscope (SeFM) for imaging fresh tissues at sub-cellular resolution. SeFM provides similar resolution and field of view (FOV) as those used during histologic analysis. The SeFM device achieved the lateral resolution of 0.57 µm and provided microscopy images over a sample area larger than 500 µm. The material cost was low, approximately $3,000. Preliminary images of human pancreatic tumor specimens clearly visualized cellular details. Quantitative analysis showed that using an excess dose of a chemotherapy drug significantly reduced the tumor-specific fluorescence signal, confirming the specificity of the drug and the detection potential of SeFM.

Single Cell Densitometry and Weightlessness Culture of Mesenchymal Stem Cells Using Magnetic Levitation

Magnetic levitation methodology enables density-based separation of microparticles/cells and sustains cell culture in different media. Levitation process can be accomplished via negative magnetophoresis (diamagnetophoresis), where the applied magnetic force compensates gravitational acceleration and the density of the diamagnetic object (e.g., cell) determines its levitation height. Here we describe a portable, sensitive, and cost-effective technology that uses the principles of magnetic levitation to measure single cell density and cell culture under desired conditions.

Emerging point-of-care technologies for sickle cell disease diagnostics

Sickle cell disease (SCD) is a serious and life-threatening disorder. SCD is considered a public health issue affecting 25% of the population in Central and West Africa. Some countries in this region lack the necessary resources to treat and diagnose many diseases including SCD. Current methods for screening SCD are time-consuming and require expensive laboratory equipment and facilities. This leads to an inability to diagnose the disease early. Lack of early diagnosis and treatment can lead to childhood death. The number of childhood deaths is significantly higher in developing countries. There is unmet need to develop novel methods for diagnosing and monitoring SCD that are both cost effective and portable. The point-of-care (POC) platforms provide the cost effectiveness and portability that allows for the potential diagnosis of millions of people in countries with few resources. In this review, we summarized the important features, benefits, limitations and potential of POC devices. We conducted a comprehensive literature analysis to compare the sensitivity and specificity of several POC diagnostics developed for SCD with a focus on their usages in resource limited settings.

Design of a 3D printed smartphone microscopic system with enhanced imaging ability for biomedical applications

Portable, low-cost smartphone platform microscopic systems have emerged as a potential tool for imaging of various micron and submicron scale particles in recent years (Ozcan; Pirnstill and Coté; Breslauer et al.; Zhu et al.). In most of the reported works, it involves either the use of sophisticated optical set-ups along with a high-end computational tool for postprocessing of the captured images, or it requires a high-end configured smartphone to obtain enhanced imaging of the sample. Present work reports the working of a low-cost, field-portable 520× optical microscope using a smartphone. The proposed smartphone microscopic system has been designed by attaching a 3D printed compact optical set-up to the rear camera of a regular smartphone. By using cloud-based services, an image processing algorithm has been developed which can be accessed anytime through a mobile broadband network. Using this facility, the quality of the captured images can be further enhanced, thus obviating the need for dedicated computational tools for postprocessing of the images. With the designed microscopic system, an optical resolution ∼2 µm has been obtained. Upon postprocessing, the resolution of the captured images can be improved further. It is envisioned that with properly designed optical set-up in 3D printer and by developing an image processing application in the cloud, it is possible to obtain a low-cost, user-friendly, field-portable optical microscope on a regular smartphone that performs at par with that of a laboratory-grade microscope. LAY DESCRIPTION: With the ever-improving features both in hardware and software part, smartphone becomes ubiquitous in the modern civilised society with approximately 8.1 billion cell phone users across the world, and ∼40% of them can be considered as smartphones. This technology is undoubtedly the leading technology of the 21st century. Very recently, various researchers across the globe have utilised different sensing components embedded in the smartphone to convert it into a field-portable low-cost and user-friendly tool which can be used for different sensing and imaging purposes. By using simple optical components such as lens, pinhole, diffuser etc. and the camera of the smartphone, various groups have converted the phone into a microscopic imaging system. Again, by removing the camera lenses of the phone, holography images of microscopic particles by directly casting its shadows on the CMOS sensor on the phone has been demonstrated. The holographic images have subsequently been processed using the dedicated computational tool, and the original photos of the samples can be obtained. All the reported smartphone-based microscopic systems either suffer from relatively low field-of-view (FOV), resolution or it needs a high computational platform. Present work, demonstrate an alternative approach by which a reasonably good resolution (<2 µm) along with high optical magnification (520×) and a large FOV (150 µm) has been obtained on a regular smartphone. For postprocessing of the captured images an image processing algorithm has been developed in the cloud and the same can be accessed by the smartphone application, obviating the need of dedicated computational tool and a high-end configured smartphone for the proposed microscope. For the development of the proposed microscopic system, a simple optical set-up has been fabricated in a 3D printer. The set-up houses all the required optical components and the sample specimen with the 3D-printed XY stage, and it can be attached easily to the rear camera of the smartphone. Using the proposed microscopic system, enhanced imaging of USAF target and red blood cells have been successfully demonstrated. With the readily available optical components and a regular smartphone, the net cost involvement is significantly low (less than $250, including the smartphone). We envisioned that the designed system could be utilised for point-of-care diagnosis in resource-poor settings where access to the laboratory facilities is very limited.

Point-of-care technologies in heart, lung, blood and sleep disorders from the Center for Advancing Point-of-Care Technologies

Recent advancements in point-of-care technologies have transformed care for patients with heart, lung, blood, and sleep disorders by providing rapid, cost-effective, and accessible solutions to challenges in the detection and management of many health conditions. However, major barriers exist throughout the technology development process that inhibit the actualization of many promising and potentially successful ideas. The Center for Advancing Point of Care Technologies has established a system for supporting further innovation in this field and bridging the gap between initial idea conception and implementation. We highlight current and emerging point-of-care technologies throughout the development spectrum and emphasize the need for a needs-driven model of health technology development that involve appropriate stakeholders in the process.

A Review of Automated Methods for the Detection of Sickle Cell Disease

Detection of sickle cell disease is a crucial job in medical image analysis. It emphasizes elaborate analysis of proper disease diagnosis after accurate detection followed by a classification of irregularities, which plays a vital role in the sickle cell disease diagnosis, treatment planning, and treatment outcome evaluation. Proper segmentation of complex cell clusters makes sickle cell detection more accurate and robust. Cell morphology has a key role in the detection of the sickle cell because the shapes of the normal blood cell and sickle cell differ significantly. This review emphasizes state-of-the-art methods and recent advances in detection, segmentation, and classification of sickle cell disease. We discuss key challenges encountered during the segmentation of overlapping blood cells. Moreover, standard validation measures that have been employed to yield performance analysis of various methods are also discussed. The methodologies and experiments in this review will be useful to further research and work in this area.

Label-free density-based detection of adipocytes of bone marrow origin using magnetic levitation

The first report on application of magnetic levitation technology for detection of adipogenic cells based on single cell density measurement.

Smartphone-based clinical diagnostics: towards democratization of evidence-based health care

Recent advancements in bioanalytical techniques have led to the development of novel and robust diagnostic approaches that hold promise for providing optimal patient treatment, guiding prevention programs and widening the scope of personalized medicine. However, these advanced diagnostic techniques are still complex, expensive and limited to centralized healthcare facilities or research laboratories. This significantly hinders the use of evidence-based diagnostics for resource-limited settings and the primary care, thus creating a gap between healthcare providers and patients, leaving these populations without access to precision and quality medicine. Smartphone-based imaging and sensing platforms are emerging as promising alternatives for bridging this gap and decentralizing diagnostic tests offering practical features such as portability, cost-effectiveness and connectivity. Moreover, towards simplifying and automating bioanalytical techniques, biosensors and lab-on-a-chip technologies have become essential to interface and integrate these assays, bringing together the high precision and sensitivity of diagnostic techniques with the connectivity and computational power of smartphones. Here, we provide an overview of the emerging field of clinical smartphone diagnostics and its contributing technologies, as well as their wide range of areas of application, which span from haematology to digital pathology and rapid infectious disease diagnostics.

Magnetic Force-Based Microfluidic Techniques for Cellular and Tissue Bioengineering

Live cell manipulation is an important biotechnological tool for cellular and tissue level bioengineering applications due to its capacity for guiding cells for separation, isolation, concentration, and patterning. Magnetic force-based cell manipulation methods offer several advantages, such as low adverse effects on cell viability and low interference with the cellular environment. Furthermore, magnetic-based operations can be readily combined with microfluidic principles by precisely allowing control over the spatiotemporal distribution of physical and chemical factors for cell manipulation. In this review, we present recent applications of magnetic force-based cell manipulation in cellular and tissue bioengineering with an emphasis on applications with microfluidic components. Following an introduction of the theoretical background of magnetic manipulation, components of magnetic force-based cell manipulation systems are described. Thereafter, different applications, including separation of certain cell fractions, enrichment of rare cells, and guidance of cells into specific macro- or micro-arrangements to mimic natural cell organization and function, are explained. Finally, we discuss the current challenges and limitations of magnetic cell manipulation technologies in microfluidic devices with an outlook on future developments in the field.

An inexpensive smartphone-based device for point-of-care ovulation testing

The ability to accurately predict ovulation at-home using low-cost point-of-care diagnostics can be of significant help for couples who prefer natural family planning. Detecting ovulation-specific hormones in urine samples and monitoring basal body temperature are the current commonly home-based methods used for ovulation detection; however, these methods, relatively, are expensive for prolonged use and the results are difficult to comprehend. Here, we report a smartphone-based point-of-care device for automated ovulation testing using artificial intelligence (AI) by detecting fern patterns in a small volume (<100 μL) of saliva that is air-dried on a microfluidic device. We evaluated the performance of the device using artificial saliva and human saliva samples and observed that the device showed >99% accuracy in effectively predicting ovulation.

Shape oscillations of single blood drops: applications to human blood and sickle cell disease

Sickle cell disease (SCD) is an inherited blood disorder associated with severe anemia, vessel occlusion, poor oxygen transport and organ failure. The presence of stiff and often sickle-shaped red blood cells is the hallmark of SCD and is believed to contribute to impaired blood rheology and organ damage. Most existing measurement techniques of blood and red blood cell physical properties require sample contact and/or large sample volume, which is problematic for pediatric patients. Acoustic levitation allows rheological measurements in a single drop of blood, simultaneously eliminating the need for both contact containment and manipulation of samples. The technique shows that the shape oscillation of blood drops is able to assess blood viscosity in normal and SCD blood and demonstrates an abnormally increased viscosity in SCD when compared with normal controls. Furthermore, the technique is sensitive enough to detect viscosity changes induced by hydroxyurea treatment, and their dependence on the total fetal hemoglobin content of the sample. Thus this technique may hold promise as a monitoring tool for assessing changes in blood rheology in sickle cell and other hematological diseases.

DNA engineered micromotors powered by metal nanoparticles for motion based cellphone diagnostics

HIV-1 infection is a major health threat in both developed and developing countries. The integration of mobile health approaches and bioengineered catalytic motors can allow the development of sensitive and portable technologies for HIV-1 management. Here, we report a platform that integrates cellphone-based optical sensing, loop-mediated isothermal DNA amplification and micromotor motion for molecular detection of HIV-1. The presence of HIV-1 RNA in a sample results in the formation of large-sized amplicons that reduce the motion of motors. The change in the motors motion can be accurately measured using a cellphone system as the biomarker for target nucleic acid detection. The presented platform allows the qualitative detection of HIV-1 (n = 54) with 99.1% specificity and 94.6% sensitivity at a clinically relevant threshold value of 1000 virus particles/ml. The cellphone system has the potential to enable the development of rapid and low-cost diagnostics for viruses and other infectious diseases.

Colorimetric-Luminance Readout for Quantitative Analysis of Fluorescence Signals with a Smartphone CMOS Sensor

Smartphones have shown promise as an enabling technology for portable and distributed point-of-care diagnostic tests. The CMOS camera sensor can be used for detecting optical signals, including fluorescence for applications such as isothermal nucleic acid amplification tests. However, such analysis is typically limited mostly to end point detection of single targets. Here we present a smartphone-based image analysis pipeline that utilizes the CIE xyY (chromaticity-luminance) color space to measure the luminance (in lieu of RGB intensities) of fluorescent signals arising from nucleic acid amplification targets, with a discrimination sensitivity (ratio between the positive to negative signals), which is an order of magnitude more than traditional RGB intensity based analysis. Furthermore, the chromaticity part of the analysis enables reliable multiplexed detection of different targets labeled with spectrally separated fluorophores. We apply this chromaticity-luminance formulation to simultaneously detect Zika and chikungunya viral RNA via end point RT-LAMP (Reverse transcription Loop-Mediated isothermal amplification). We also show real time LAMP detection of Neisseria gonorrhoeae samples down to a copy number of 3.5 copies per 10 μL of reaction volume in our smartphone-operated portable LAMP box. Our chromaticity-luminance analysis is readily adaptable to other types of multiplexed fluorescence measurements using a smartphone camera.