For modern approaches in precision medicine, fast and easy-to-use point-of-care diagnostics (POCs) are essential. Digoxin was chosen as an example of a drug requiring close monitoring. Digoxin is a cardiac glycoside used for the treatment of tachycardia with a narrow therapeutic window of 0.5–2.0 ng·mL−1, and toxic effects are common for concentrations above 2.5 ng·mL−1. For monitoring of blood concentration levels and treatment of intoxication, highly selective antibodies for digoxin and its hapten, digoxigenin, are available. A smartphone readout system is described for measuring digoxigenin in human serum using a common gold nanoparticle lateral flow assay (LFA). The R-package GNSplex, which also includes a Shiny app for quantitative test interpretation based on linear models, is used for image analysis. Images of lateral flow strips were taken with an iPhone camera and a simple darkbox made from black cardboard. Sensitivity and accuracy of the quantitative smartphone system as well as analytical parameters such as limit of detection (LOD) were determined and compared to data obtained with a high resolution BioImager. The data show that the smartphone based digoxin assay yields reliable quantitative results within the clinically relevant concentration range. Graphical abstractFor therapeutic drug monitoring and point of care diagnostics we introduce the open source R-package GNSplex for smartphone readout and interpretation of lateral flow assays. The cardiac glycoside dixogin was used as target for this quantitative smartphone reader Electronic supplementary materialThe online version of this article (10.1007/s00604-018-3195-6) contains supplementary material, which is available to authorized users.

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Fast point-of-care (POC) diagnostics represent an unmet medical need and include applications such as lateral flow assays (LFAs) for the diagnosis of sepsis and consequences of cytokine storms and for the treatment of COVID-19 and other systemic, inflammatory events not caused by infection. Because of the complex pathophysiology of sepsis, multiple biomarkers must be analyzed to compensate for the low sensitivity and specificity of single biomarker targets. Conventional LFAs, such as gold nanoparticle dyed assays, are limited to approximately five targets—the maximum number of test lines on an assay. To increase the information obtainable from each test line, we combined green and red emitting quantum dots (QDs) as labels for C-reactive protein (CRP) and interleukin-6 (IL-6) antibodies in an optical duplex immunoassay. CdSe-QDs with sharp and tunable emission bands were used to simultaneously quantify CRP and IL-6 in a single test line, by using a single UV-light source and two suitable emission filters for readout through a widely available BioImager device. For image and data processing, a customized software tool, the MultiFlow-Shiny app was used to accelerate and simplify the readout process. The app software provides advanced tools for image processing, including assisted extraction of line intensities, advanced background correction and an easy workflow for creation and handling of experimental data in quantitative LFAs. The results generated with our MultiFlow-Shiny app were superior to those generated with the popular software ImageJ and resulted in lower detection limits. Our assay is applicable for detecting clinically relevant ranges of both target proteins and therefore may serve as a powerful tool for POC diagnosis of inflammation and infectious events.

Point-of-care (POC) diagnostics, in particular lateral flow assays (LFA), represent a great opportunity for rapid, precise, low-cost and accessible diagnosis of disease. Especially with the ongoing coronavirus disease 2019 (COVID-19) pandemic, rapid point-of-care tests are becoming everyday tools for identification and prevention. Using smartphones as biosensors can enhance POC devices as portable, low-cost platforms for healthcare and medicine, food and environmental monitoring, improving diagnosis and documentation in remote, low-resource locations. We present an open-source, all-in-one smartphone-based system for quantitative analysis of LFAs. It consists of a 3D-printed photo box, a smartphone for image acquisition, and an R Shiny software package with modular, customizable analysis workflow for image editing, analysis, data extraction, calibration and quantification of the assays. This system is less expensive than commonly used hardware and software, so it could prove very beneficial for diagnostic testing in the context of pandemics, as well as in low-resource countries.

Semiconductor nanoparticles, especially quantum dots (QDs), exhibit favourable optical properties for fluorescence imaging. Simultaneous excitation, without the need for monochromatic light and sharp emission bands allow the development of fast and sensitive multiplex immunoassays. Nano dyes can replace conventional organic dyes to increase the number of signals for multiplexing or be used in combination to form effective FRET-pairs. Advantageous properties like resistance to photobleaching or long fluorescence lifetimes at stable quantum yields and high extinction coefficients add to the benefits of quantum dots. Different strategies for data acquisition and experimental setup can improve conventional staining techniques to make them more sensitive, faster or versatile in multicolour imaging. The photostability allows long term light exposure of quantum dots, increasing the time frame for applications like live cell imaging. We provide a brief overview on current fluorescent tags and hardware suitable for multiplexing.

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Point-of-care (POC) diagnostics, in particular lateral flow assays (LFA), represent a great opportunity for rapid, precise, low-cost and accessible diagnosis of disease. Especially with the ongoing coronavirus disease 2019 (COVID-19) pandemic, rapid point-of-care tests are becoming everyday tools for identification and prevention. Using smartphones as biosensors can enhance POC devices as portable, low-cost POC platforms for healthcare and medicine, food and environmental monitoring, improving diagnosis and documentation in remote, low-income locations. We present an open-source, all-in-one smartphone-based system for quantitative analysis of LFAs. It consists of a 3D-printed photo box, a smartphone for image acquisition, and an R Shiny software package with modular, customizable analysis workflow for image editing, analysis, data extraction, calibration and quantification of the assays. This system is less expensive than commonly used hardware and software, so it could prove very beneficial for diagnostic testing in the context of pandemics, as well as in low-resource countries.