Deep Learning-Enabled Multiplexed Point-of-Care Sensor using a Paper-Based Fluorescence Vertical Flow Assay.

Multiplexed computational sensing with a point-of-care serodiagnosis assay to simultaneously quantify three biomarkers of acute cardiac injury is demonstrated. This point-of-care sensor includes a paper-based fluorescence vertical flow assay (fxVFA) processed by a low-cost mobile reader, which quantifies the target biomarkers through trained neural networks, all within <15 min of test time using 50 µL of serum sample per patient. This fxVFA platform is validated using human serum samples to quantify three cardiac biomarkers, i.e., myoglobin, creatine kinase-MB, and heart-type fatty acid binding protein, achieving less than 0.52 ng mL-1 limit-of-detection for all three biomarkers with minimal cross-reactivity. Biomarker concentration quantification using the fxVFA that is coupled to neural network-based inference is blindly tested using 46 individually activated cartridges, which shows a high correlation with the ground truth concentrations for all three biomarkers achieving >0.9 linearity and <15% coefficient of variation. The competitive performance of this multiplexed computational fxVFA along with its inexpensive paper-based design and handheld footprint makes it a promising point-of-care sensor platform that can expand access to diagnostics in resource-limited settings.

Vertical Flow Assay for Inflammatory Biomarkers Based on Nanofluidic Channel Array and SERS Nanotags.

There is a great demand for the development of detection assays for inflammation infection diagnosis with high throughput and ultrasensitivity. Herein, a vertical flow assay system with functionalized nanoporous anodic aluminum oxide (AAO) as sensing membrane, and encoded core-shell surface enhanced Raman scattering (SERS) nanotags as labels for multiple inflammatory biomarkers detection is presented. A 2 × 2 test array on the porous AAO is developed and modified with multiple capture antibodies to capture inflammatory biomarkers from samples. Due to the high surface area to volume ratio of the AAO membrane, and its influence on plasmonic coupling, the electromagnetic field of the encoded core-shell SERS nanotags is enhanced. Detection limits of 53.4, 4.72, 48.3, and 7.53 fg mL-1 are realized for C reactive protein, interleukin-6, serum amyloid A, and procalcitonin, respectively, with a linear dynamic range spanning at least five orders of magnitude. In addition, the proposed method also shows acceptable accuracy and repeatability for the detection of clinical samples. Therefore, this approach is expected to be a powerful point of care testing tool for disease diagnosis in facility limited areas.

A Paper-Based Multiplexed Serological Test to Monitor Immunity against SARS-COV-2 Using Machine Learning.

The rapid spread of SARS-CoV-2 caused the COVID-19 pandemic and accelerated vaccine development to prevent the spread of the virus and control the disease. Given the sustained high infectivity and evolution of SARS-CoV-2, there is an ongoing interest in developing COVID-19 serology tests to monitor population-level immunity. To address this critical need, we designed a paper-based multiplexed vertical flow assay (xVFA) using five structural proteins of SARS-CoV-2, detecting IgG and IgM antibodies to monitor changes in COVID-19 immunity levels. Our platform not only tracked longitudinal immunity levels but also categorized COVID-19 immunity into three groups: protected, unprotected, and infected, based on the levels of IgG and IgM antibodies. We operated two xVFAs in parallel to detect IgG and IgM antibodies using a total of 40 µL of human serum sample in <20 min per test. After the assay, images of the paper-based sensor panel were captured using a mobile phone-based custom-designed optical reader and then processed by a neural network-based serodiagnostic algorithm. The serodiagnostic algorithm was trained with 120 measurements/tests and 30 serum samples from 7 randomly selected individuals and was blindly tested with 31 serum samples from 8 different individuals, collected before vaccination as well as after vaccination or infection, achieving an accuracy of 89.5%. The competitive performance of the xVFA, along with its portability, cost-effectiveness, and rapid operation, makes it a promising computational point-of-care (POC) serology test for monitoring COVID-19 immunity, aiding in timely decisions on the administration of booster vaccines and general public health policies to protect vulnerable populations.

Microporous affinity membranes and their incorporation into microfluidic devices for monitoring of therapeutic antibodies.

Control of monoclonal antibody (mAb) concentrations in serum is important for maintaining the safety and efficacy of these lifesaving therapeutics. Point-of-care (POC) quantification of therapeutic mAbs could ensure that patients have effective mAb levels without compromising safety. This work uses mimotope-functionalized microporous alumina affinity membranes in vertical flow assays for detection and quantitation of therapeutic mAbs. Selective capture of bevacizumab from 1000:1 diluted serum or plasma and binding of a fluorescently labelled anti-human IgG secondary antibody enable fluorescence-based analysis of bevacizumab at its therapeutically relevant concentration range of ∼50-300 µg/mL. The assay results in a linear relationship between the fluorescence intensity of the antibody capture spot and the bevacizumab concentration. A simple prototype microfluidic device containing these membranes allows washing, reagent additions and visualization of signal within 15 min using a total of 5 mL of fluid. The prototype devices can monitor physiologically relevant bevacizumab levels in diluted serum, and future refinements might lead to a POC device for therapeutic drug monitoring.

Lateral and Vertical Flow Assays for Point-of-Care Diagnostics.

Lateral flow assays (LFAs) have been the pillar of rapid point-of-care (POC) diagnostics due to their simplicity, rapid process, and low cost. Recent advances in sensitivity, selectivity, and chemical stability enhancement have ensured the foothold of LFAs in commercial POC diagnostics. This paper reviews recent developments in labeling strategies and detection methods of LFAs. Moreover, vertical flow assays (VFAs) have emerged as an alternate paper-based assay due to faster detection time and unique multiplexing capabilities. Smartphones as LFA readers have been transformed into a universal integrated platform for imaging, data processing, and storage, providing quantitative results in low-resource settings. Commercial LFAs and VFAs products are evaluated with regards to their performance, market trends, and regulatory issues. The future outlook of the flow-based assays for POC diagnostics is also discussed.