Point-of-care Colorimetric Analysis through Smartphone Video

A dual-ligand lanthanide-based metal-organic framework for highly selective and sensitive colorimetric detection of Fe2

Accumulation of heavy metals in humans and mammals causes health problems due to their abundance as transition metal ions. Iron (Fe2+) serves significantly in numerous biological processes as a heavy metal ion. In this study, we have designed and prepared a metal-organic framework (MOF) utilizing a one-step solvothermal process, incorporating a dual-ligand combination of terephthalic acid (H2BDC) and α,α',α''-tert-pyridine (TPY) with Eu3+ as the metal node. For this MOF, we termed it Eu-BDC/TPY. Eu-BDC/TPY has superior selectivity over other metal cations. It provides an accurate, sensitive, broad linear range colorimetric method for detecting Fe2+ in a concentration range of 1-50 μM with a modest limit of detection (0.33 μM). Eu-BDC/TPY detects the absence of Fe2+ quickly (within 5 seconds), which is very valuable in practical applications. In addition, the results can be used to create a digital image colorimetric card (DIC) using colorimetric software, enabling instantaneous detection of Fe2+ concentration using a smartphone.

Coupling ELISA to smartphones for POCT of chronic and congenital Chagas disease

Chagas disease (CD) affects about 7 million people worldwide, presents a large prevalence in Latin America, and is growing in the rest of the world, where congenital CD is the main mode of transmission. Point-of-care testing (POCT) methods are increasingly required to ease early diagnostics and increase treatment success. This work presents the development and validation of a smartphone-integrated ELISA-based POCT system for the detection of both chronic and congenital CD. Expensive and bulky equipment used for ELISA in conventional laboratories was replaced as follows. A miniaturized device was fabricated for incubation of commercial ELISA plates, achieving ∼±1 °C uniformity and stability. The ELISA plate reader was replaced by smartphone camera and image processing, comprising algorithms to account for variability sources and spatial light non-uniformity; thus, additional hardware like a dark-box is not required. The agreement between samples classified with this novel reading method vs. ELISA plate reader was found to be 99.7% and 95.4% for chronic and congenital CD, respectively. Furthermore, a smartphone application was designed and implemented to guide the user during the assay, provide connectivity, and access databases, facilitating patient monitoring and health-policy making. The whole system is aimed to be used as a practical diagnostic tool in primary health care settings, as well as to facilitate patients' follow-up to provide better treatment. Concerning the technology itself, the proposed POCT platform is versatile enough to be readily adapted for the detection of other infectious diseases.

Deep learning-assisted ultra-accurate smartphone testing of paper-based colorimetric ELISA assays

Smartphone has long been considered as one excellent platform for disease screening and diagnosis, especially when combined with microfluidic paper-based analytical devices (μPADs) that feature low cost, ease of use, and pump-free operations. In this paper, we report a deep learning-assisted smartphone platform for ultra-accurate testing of paper-based microfluidic colorimetric enzyme-linked immunosorbent assay (c-ELISA). Different from existing smartphone-based μPAD platforms, whose sensing reliability is suffered from uncontrolled ambient lighting conditions, our platform is able to eliminate those random lighting influences for enhanced sensing accuracy. We first constructed a dataset that contains c-ELISA results (n = 2048) of rabbit IgG as the model target on μPADs under eight controlled lighting conditions. Those images are then used to train four different mainstream deep learning algorithms. By training with these images, the deep learning algorithms can well eliminate the influences of lighting conditions. Among them, the GoogLeNet algorithm gives the highest accuracy (>97%) in quantitative rabbit IgG concentration classification/prediction, which also provides 4% higher area under curve (AUC) value than that of the traditional curve fitting results analysis method. In addition, we fully automate the whole sensing process and achieve the "image in, answer out" to maximize the convenience of the smartphone. A simple and user-friendly smartphone application has been developed that controls the whole process. This newly developed platform further enhances the sensing performance of μPADs for use by laypersons in low-resource areas and can be facilely adapted to the real disease protein biomarkers detection by c-ELISA on μPADs.

Smartphone-Facilitated Mobile Colorimetric Probes for Rapid Monitoring of Chemical Contaminations in Food: Advances and Outlook

Smartphone-derived colorimetric tools have the potential to revolutionize food safety control by enabling citizens to carry out monitoring assays. To realize this, it is of paramount significance to recognize recent study efforts and figure out important technology gaps in terms of food security. Driven by international connectivity and the extensive distribution of smartphones, along with their built-in probes and powerful computing abilities, smartphone-based sensors have shown enormous potential as cost-effective and portable diagnostic scaffolds for point-of-need tests. Meantime, the colorimetric technique is of particular notice because of its benefits of rapidity, simplicity, and high universality. In this study, we tried to outline various colorimetric platforms using smartphone technology, elucidate their principles, and explore their applications in detecting target analytes (pesticide residues, antibiotic residues, metal ions, pathogenic bacteria, toxins, and mycotoxins) considering their sensitivity and multiplexing capability. Challenges and desired future perspectives for cost-effective, accurate, reliable, and multi-functions smartphone-based colorimetric tools have also been debated.

A regression-based machine learning approach for pH and glucose detection with redox-sensitive colorimetric paper sensors

Colorimetric paper sensors are used in various fields due to their convenience and intuitive manner. However, these sensors present low accuracy in practical use because it is difficult to distinguish color changes for a minute amount of analyte with the naked eye. Herein, we demonstrate that a machine learning (ML)-based paper sensor platform accurately determines the color changes. We fabricated a colorimetric paper sensor by adsorbing polyaniline nanoparticles (PAni-NPs), whose color changes from blue to green when the ambient pH decreases. Adding glucose oxidase (GOx) to the paper sensor enables colorimetric glucose detection. Target analytes (10 μL) were aliquoted onto the paper sensors, and their images were taken with a smartphone under the same conditions in a darkroom. The red-green-blue (RGB) data from the images were extracted and used to train and test three regression models: support vector regression (SVR), decision tree regression (DTR), and random forest regression (RFR). Of the three regression models, RFR performed the best at estimating pH levels (R² = 0.957) ranging from pH 2 to 10 and glucose concentrations (R² = 0.922) ranging from 0 to 10 mg mL^-1.

Microfluidic Devices for HIV Diagnosis and Monitoring at Point-of-Care (POC) Settings

Human immunodeficiency virus (HIV) is a global epidemic; however, many individuals are able to obtain treatment and manage their condition. Progression to acquired immunodeficiency syndrome (AIDS) occurs during late-stage HIV infection, which compromises the immune system, making it susceptible to infections. While there is no cure, antiretroviral therapy can be used provided that detection occurs, preferably during the early phase. However, the detection of HIV is expensive and resource-intensive when tested with conventional methods, such as flow cytometry, polymerase chain reaction (PCR), or enzyme-linked immunosorbent assays (ELISA). Improving disease detection in resource-constrained areas requires equipment that is affordable, portable, and can deliver rapid results. Microfluidic devices have transformed many benchtop techniques to on-chip detection for portable and rapid point-of-care (POC) testing. These devices are cost-effective, sensitive, and rapid and can be used in areas lacking resources. Moreover, their functionality can rival their benchtop counterparts, making them efficient for disease detection. In this review, we discuss the limitations of currently used conventional HIV diagnostic assays and provide an overview of potential microfluidic technologies that can improve HIV testing in POC settings.

A free customizable tool for easy integration of microfluidics and smartphones

The integration of smartphones and microfluidics is nowadays the best possible route to achieve effective point-of-need testing (PONT), a concept increasingly demanded in the fields of human health, agriculture, food safety, and environmental monitoring. Nevertheless, efforts are still required to integrally seize all the advantages of smartphones, as well as to share the developments in easily adoptable formats. For this purpose, here we present the free platform appuente that was designed for the easy integration of microfluidic chips, smartphones, and the cloud. It includes a mobile app for end users, which provides chip identification and tracking, guidance and control, processing, smart-imaging, result reporting and cloud and Internet of Things (IoT) integration. The platform also includes a web app for PONT developers, to easily customize their mobile apps and manage the data of administered tests. Three application examples were used to validate appuente: a dummy grayscale detector that mimics quantitative colorimetric tests, a root elongation assay for pesticide toxicity assessment, and a lateral flow immunoassay for leptospirosis detection. The platform openly offers fast prototyping of smartphone apps to the wide community of lab-on-a-chip developers, and also serves as a friendly framework for new techniques, IoT integration and further capabilities. Exploiting these advantages will certainly help to enlarge the use of PONT with real-time connectivity in the near future.

Improving the Quantification of Colorimetric Signals in Paper-Based Immunosensors with an Open-Source Reader

Measuring the colorimetric signals produced by the biospecific accumulation of colorimetric probes and recording the results is a key feature for next-generation paper-based rapid tests. Manual processing of these tests is time-consuming and prone to a loss of accuracy when interpreting faint and patchy signals. Proprietary, closed-source readers and software companies offering automated smartphone-based assay readings have both been criticized for interoperability issues. Here, we introduce a minimal reader prototype composed of open-source hardware and open-source software that has the benefits of automatic assay quantification while avoiding the interoperability issues associated with closed-source readers. An image-processing algorithm was developed to automate the selection of an optimal region of interest and measure the average pixel intensity. When used to quantify signals produced by lateral flow immunoassays for detecting antibodies against SARS-CoV-2, results obtained with the proposed algorithm were comparable to those obtained with a manual method but with the advantage of improving the precision and accuracy when quantifying small spots or faint and patchy signals.

Dengue Detection: Advances in Diagnostic Tools from Conventional Technology to Point of Care

The dengue virus (DENV) is a vector-borne flavivirus that infects around 390 million individuals each year with 2.5 billion being in danger. Having access to testing is paramount in preventing future infections and receiving adequate treatment. Currently, there are numerous conventional methods for DENV testing, such as NS1 based antigen testing, IgM/IgG antibody testing, and Polymerase Chain Reaction (PCR). In addition, novel methods are emerging that can cut both cost and time. Such methods can be effective in rural and low-income areas throughout the world. In this paper, we discuss the structural evolution of the virus followed by a comprehensive review of current dengue detection strategies and methods that are being developed or commercialized. We also discuss the state of art biosensing technologies, evaluated their performance and outline strategies to address challenges posed by the disease. Further, we outline future guidelines for the improved usage of diagnostic tools during recurrence or future outbreaks of DENV.

Smartphone-based technique for the determination of a titration equivalence point from an RGB linear-segment curve with an example application to miniaturized titration of sodium chloride injections

A smartphone-based technique for determining the titration equivalence point from a linear-segment curve was developed for the first time. In this method, a titrant in an increasing microliter-volume was added to a set of sample aliquots containing an indicator covering both sides of the equivalence point. The solutions were subsequently photographed in one shot, in a dark box using a smartphone camera and an illuminating screen of a tablet or light emitting diode lamps arranged below a white acrylic sheet as a light source. After the colors of the solutions were delineated to Red, Green, and Blue (RGB) values, 1/log G was used to construct a plot in which the equivalence point was located at the intersection of the two lines in the region before and after the equivalence point. The technique was successfully applied to the miniaturized titration of sodium chloride injections, showing the good linear relationship of equivalence points to the sodium chloride concentration in the range of 0.4163-0.9675% w/v (R² of 0.9998). The assay was accurate (% recovery of 98.92-100.52), precise (% relative standard deviation ≤ 1.20), and unaffected by the use of different types of microplates, smartphones, and RGB analysis tools. Additionally, it required no expensive nor complicated equipment and offered the possibility of performing analysis on a single smartphone device when it was used with a mobile application developed to aid data processing and immediate production of reports of analytical results.

Management of COVID-19: current status and future prospects

COVID-19, a highly transmissible pandemic disease, is affecting millions of lives around the world. Severely infected patients show acute respiratory distress symptoms. Sustainable management strategies are required to save lives of the infected people and further preventing spread of the virus. Diagnosis, treatment, and vaccination development initiatives are already exhibited from the scientific community to fight against this virus. In this review, we primarily discuss the management strategies including prevention of spread, prophylaxis, vaccinations, and treatment for COVID-19. Further, analysis of vaccine development status and performance are also briefly discussed. Global socioeconomic impact of COVID-19 is also analyzed as part of this review.

Diagnosis for COVID-19: current status and future prospects

Introduction: Coronavirus disease 2019 (COVID-19), a respiratory illness caused by novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), had its first detection in December 2019 in Wuhan (China) and spread across the world. In March 2020, the World Health Organization (WHO) declared COVID-19 a pandemic disease. The utilization of prompt and accurate molecular diagnosis of SARS-CoV-2 virus, isolating the infected patients, and treating them are the keys to managing this unprecedented pandemic. International travel acted as a catalyst for the widespread transmission of the virus.Areas covered: This review discusses phenotype, structural, and molecular evolution of recognition elements and primers, its detection in the laboratory, and at point of care. Further, market analysis of commercial products and their performance are also evaluated, providing new ways to confront the ongoing global public health emergency.Expert commentary: The outbreak for COVID-19 created mammoth chaos in the healthcare sector, and still, day by day, new epicenters for the outbreak are being reported. Emphasis should be placed on developing more effective, rapid, and early diagnostic devices. The testing laboratories should invest more in clinically relevant multiplexed and scalable detection tools to fight against a pandemic like this where massive demand for testing exists.

A microfluidic biosensor for rapid and automatic detection of Salmonella using metal-organic framework and Raspberry Pi

Rapid screening of pathogenic bacteria contaminated foods is crucial to prevent food poisoning. However, available methods for bacterial detection are still not ready for in-field screening because culture is time-consuming; PCR requires complex DNA extraction and ELISA lacks sensitivity. In this study, a microfluidic biosensor was developed for rapid, sensitive and automatic detection of Salmonella using metal-organic framework (MOF) NH₂-MIL-101(Fe) with mimic peroxidase activity to amplify biological signal and Raspberry Pi with self-developed App to analyze color image. First, the target bacteria were separated and concentrated with the immune magnetic nanobeads (MNBs), and labeled with the immune MOFs to form MNB-Salmonella-MOF complexes. Then, the complexes were used to catalyze colorless o-phenylenediamine and H₂O₂ to produce yellow 2,3-diaminophenazine (DAP). Finally, the image of the catalysate was collected under the narrow-band blue light and analyzed using the Raspberry Pi App to determine the bacterial concentration. The experimental results showed that this biosensor was able to detect Salmonella Typhimurium from 1.5 × 10¹ to 1.5 × 10⁷ CFU/mL in 1 h with the lower detection limit of 14 CFU/mL. The mean recovery for Salmonella in spiked chicken meats was ~112%. This biosensor integrating mixing, separation, labelling and detection onto a single microfluidic chip has demonstrated the merits of automatic operation, fast reaction, less reagent and small size, and is promising for in-field detection of foodborne bacteria.

Video colorimetry of single-chromophore systems based on vector analysis in the 3D color space: Unexpected hysteresis loops in oscillating chemical reactions

Colorimetry, the quantitative determination of color, usually of a digital image, has useful applications in diverse areas of research. Many methods have been proposed for translating the RGB data of an image to obtain concentration information. Among the many methods for RGB analysis, we focus on the vector projection method (VP), which is based on a vector analysis in 3D RGB color space. This method has the major advantages of being conceptually intelligible and generalizable to various systems. For solutions with variable concentrations of one chromophore, we will show that the analysis of the trace in RGB color space allows for a judgment about the reliability of the linear concentration dependence of the chromapostasi parameter. We discuss the theoretical underpinnings of the method in two test cases, a simple dye solution and a titration of an organic acid with phenolphthalein indicator. The VP method was then applied to the Ce-catalyzed Belousov-Zhabotinsky reaction with the expectation that the colorimetry would quantify [Ce⁴⁺] oscillations. Surprisingly, the 3D color space analysis revealed hysteresis loops and the origin and implications of this observation are discussed.

High-Performance Estimation of Lead Ion Concentration Using Smartphone-Based Colorimetric Analysis and a Machine Learning Approach

Traditional methods for detection of lead ions in water samples are costly and time-consuming. In this work, an accurate smartphone-based colorimetric sensor was developed utilizing a novel machine learning algorithm. In the presence of Pb²⁺ ions in the solution of specifically functionalized gold nanoparticles, the color of solution turns from red to purple. Indeed, the color variation of the solution is proportional to Pb²⁺ concentration. The smartphone camera captures the corresponding color change, and the image is processed by an efficient artificial intelligence protocol. The nonlinear regression approach was used for concentration estimation, in which the parameters of the proposed model are obtained using a new feature extraction algorithm. In prediction of Pb²⁺ concentration, the average absolute error and root-mean-square error were 0.094 and 0.124, respectively. The influence of pH of the medium, temperature, oligonucleotide concentration, and reaction time on the performance of the proposed sensor was carefully investigated and understood to achieve the best sensor response. This novel sensor exhibited good linearity for the detection of Pb²⁺ in the concentration range of 0.5-2000 ppb with a detection limit of 0.5 ppb.

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.

Advances in HIV diagnosis and monitoring

Although highly active antiretroviral therapy (HAART) has been introduced over twenty years ago to treat Human Immunodeficiency Virus (HIV) positive patients, acquired immunodeficiency syndrome (AIDS) is still one of the deadliest diseases found worldwide. AIDS prevalence and mortality rates are usually more pronounced in resource-constrained countries than in the developed world. The lack of trained medical technicians, sophisticated diagnostic equipment, and the overall scarcity of medical infrastructures have severely impacted HIV/AIDS diagnostics, which hinders the initiation and periodic monitoring of antiretroviral therapy (ART). Currently, available HIV viral load assays are not well-suited for resource-limited settings due to their high cost and a requirement for medical/technical infrastructures. In this paper, we review current and emerging diagnostic assays for HIV detection, with a focus on point-of-care (POC) based immunoassays for viral load measurement, drug resistance, and HIV recurrence. We also discuss the limitations of the available HIV assays and highlight the technological advancements in cellphone, paper, and flexible material-based assays which have the potential to improve HIV diagnosis and monitoring, thus assisting with the management of the disease.

Color Space Transformation-Based Algorithm for Evaluation of Thermochromic Behavior of Cholesteric Liquid Crystals Using Polarized Light Microscopy

Cholesteryl ester liquid crystals exhibit thermochromic properties related to the existence of a twisted nematic phase. When used in applications such as thermal mapping, a color change is often monitored by video cameras. Thus, quantitative methods to evaluate thermochromic behavior (e.g., blue-start, red-start, red-end, color play and bandwidth) from video analysis are desirable. However, obtaining quantitative color measurements from digital images remains a significant technical challenge, especially for highly reflective samples such as liquid crystals (for which ultraviolet-visible (UV-vis) reflectance spectroscopy is typically used). We developed a method to determine thermochromic properties from videos of liquid crystal cooling under polarized light microscopy. We relate observed color transitions to quantifiable changes in the cumulative color difference in the International Commission on Illumination (CIE) L*a*b* color space and validate this method with UV-vis reflectance spectroscopy. The measured thermochromic behavior and associated measurement uncertainties (coefficient of variations) were comparable to UV-vis reflectance measurements.

A smartphone-based biomedical sensory system

Disease diagnostics, food safety monitoring and environmental quality monitoring are the key means to safeguard human health. However, conventional detection devices for health care are costly, bulky and complex, restricting their applications in resource-limited areas of the world. With the rapid development of biosensors and the popularization of smartphones, smartphone-based sensing systems have emerged as novel detection devices that combine the sensitivity of biosensors and diverse functions of smartphones to provide a rapid, low-cost and convenient detection method. In these systems, a smartphone is used as a microscope to observe and count cells, as a camera to record fluorescence images, as an analytical platform to analyze experimental data, and as an effective tool to connect detection devices and online doctors. These systems are widely used for cell analysis, biochemical analysis, immunoassays, and molecular diagnosis, which are applied in the fields of disease diagnostics, food safety monitoring and environmental quality monitoring. Therefore, we discuss four types of smartphone-based sensing systems in this review paper, specifically in terms of the structure, performance and efficiency of these systems. Finally, we give some suggestions for improvement and future prospective trends.

Development of a Flow-Free Automated Colorimetric Detection Assay Integrated with Smartphone for Zika NS1

The Zika virus (ZIKV) is an emerging flavivirus transmitted to humans by Aedes mosquitoes that can potentially cause microcephaly, Guillain–Barré Syndrome, and other birth defects. Effective vaccines for Zika have not yet been developed. There is a necessity to establish an easily deployable, high-throughput, low-cost, and disposable point-of-care (POC) diagnostic platform for ZIKV infections. We report here an automated magnetic actuation platform suitable for a POC microfluidic sandwich enzyme-linked immunosorbent assay (ELISA) using antibody-coated superparamagnetic beads. The smartphone integrated immunoassay is developed for colorimetric detection of ZIKV nonstructural protein 1 (NS1) antigen using disposable chips to accommodate the reactions inside the chip in microliter volumes. An in-house-built magnetic actuator platform automatically moves the magnetic beads through different aqueous phases. The assay requires a total of 9 min to automatically control the post-capture washing, horseradish peroxidase (HRP) conjugated secondary antibody probing, washing again, and, finally, color development. By measuring the saturation intensity of the developed color from the smartphone captured video, the presented assay provides high sensitivity with a detection limit of 62.5 ng/mL in whole plasma. These results advocate a great promise that the platform would be useful for the POC diagnosis of Zika virus infection in patients and can be used in resource-limited settings.

Smartphone-integrated urinary CTX-II immunosensor based on wavelength filtering from chromogenic reaction

The integration of smart IT devices and biochemical assays with optical biosensing technology facilitates the development of efficacious optical biosensors for many practical diagnostic fields, owing to their minimized use of high-technical electronic components and simple operation. Herein, we introduced a simple optical biosensing system based on the specific wavelength filtering principle and count-based analysis method. The developed system uses a smartphone with a paper-based signal guide and a biosensing channel. The paper-based signal guide was prepared by printing red patterns of various brightness on a black background. Given that a blue product is generated as a result of horseradish peroxidase (HRP)-based enzymatic reaction in the biosensing channel, the channel could be used as a blue filter that absorbs red light. When red light reflected from the red pattern is absorbed by the channel, the pattern appears black. As such, the color of the patterns is assimilated with the black background, so it seems to disappear. Consequently, the amount of blue product relative to the concentration of the target analyte can be measured by counting the number of observed patterns on the paper-based signal guide. In this study, the concentration of urinary C-telopeptide fragment of type II collagen (uCTX-II, 0-10 ng/mL) was measured using the developed system without complicated equipment. In addition, the quantitative analysis of uCTX-II in the real urine sample was successfully performed. Therefore, we expect that the developed optical transducing system could be practically used for point-of-care testing (POCT) diagnosis under resource-limited environmental conditions.

Microfluidic Chip for Detection of Fungal Infections

Fungal infections can lead to severe clinical outcomes such as multiple organ failure and septic shock. Rapid detection of fungal infections allows clinicians to treat patients in a timely manner and improves clinical outcomes. Conventional detection methods include blood culture followed by plate culture and polymerase chain reaction. These methods are time-consuming and require expensive equipment, hence, they are not suitable for point-of-care and clinical settings. There is an unmet need to develop a rapid and inexpensive detection method for fungal infections such as candidemia. We developed an innovative immuno-based microfluidic device that can rapidly detect and capture Candida albicans from phosphate-buffered saline (PBS) and human whole blood. Our microchip technology showed an efficient capture of C. albicans in PBS with an efficiency of 61-78% at various concentrations ranging from 10 to 10⁵ colony-forming units per milliliter (cfu/mL). The presented microfluidic technology will be useful to screen for various pathogens at the point-of-care and clinical settings.

Cell phone based colorimetric analysis for point-of-care settings

Cell phones show considerable promise for point-of-care (POC) diagnostic procedures because they are accessible, connected, and computationally powerful. Cell phone image processing methods are being developed for the detection and quantification of a wide range of targets, employing methods from microscopy to fluorescence techniques. However, most of the lab-based biological and biochemical assays still lack a robust and repeatable cell phone analogue. Existing solutions require external smartphone hardware to obtain quantitative results, imposing a design tradeoff between accessibility and accuracy. Here, we develop a cell phone imaging algorithm that enables analysis of assays that would typically be evaluated via spectroscopy. The developed technique uses the saturation parameter of hue-saturation-value color space to enable POC diagnosis. Through the analysis of over 10 000 images, we show that the saturation method consistently outperforms existing algorithms under a wide range of operating field conditions. The performance improvement is also proven analytically via the mathematic relationship between the saturation method and existing techniques. The method presented here is a step forward towards the development of POC diagnostics by reducing the required equipment, improving the limit of detection (LOD), and increasing the precision of quantitative results.

Development of a Flow-Free Magnetic Actuation Platform for an Automated Microfluidic ELISA

There is a need to create an easily deployable and point-of-care (POC) diagnostic platform for disease outbreaks and for monitoring and maintenance of chronic illnesses. Such platforms are useful in regions where access to clinical laboratories may be limited or constrained using cost-effective solutions to quickly process high numbers of samples. Using oil and water liquid–liquid interphase separation, immunoassays developed for microfluidic chips can potentially meet this need when leveraged with electromagnetic actuation and antibody-coated superparamagnetic beads. We have developed a microfluidic immunoassay detection platform, which enables assay automation and maintains successful liquid containment for future use in the field. The assay was studied through a series of magnetic and fluid simulations to demonstrate these optimizations, and an optimized chip was tested using a target model for HIV-1, the p24 capsid antigen. The use of minimal reagents further lowers the cost of each assay and lowers the required sample volume for testing (<50 μL), that can offer easy turnaround for sample collection and assay results. The developed microfluidic immunoassay platform can be easily scaled for multiplex or multi-panel specific testing at the POC.

A flow-free device is developed for automated and rapid ELISA testing at the point-of-care settings.