We report a point-of-care sensor for multiplexed quantification of three cardiac biomarkers, i.e., myoglobin, creatine kinase-MB (CK-MB) and heart-type fatty acid binding protein (FABP) from human serum. The sensor uses a paper-based fluorescent vertical-flow assay (fxVFA), and the assay operation takes <15 min and requires 50 µL of serum. After the assay, an image of the sensing membrane is captured by a cellphone-based reader, and a deep learning-based algorithm infers the concentrations of the 3 cardiac biomarkers from the captured fluorescence image. Our fxVFA achieved a limit-of-detection of <0.52 ng/mL, a coefficient-of-determination of >0.9, and a coefficient-of-variation (CV) of <15%.
We demonstrate a multiplexed fluorescent vertical flow assay (fxVFA) processed by a hand-held reader and a deep learning-based algorithm for quantification of three biomarkers, i.e. myoglobin, creatine kinase-MB (CK-MB) and heart-type fatty acid binding protein (FABP) from human serum samples. fxVFA operation takes <15 min and requires 50 µL of serum sample. fxVFA achieved <0.52 ng/mL limits-of-detection for all three analytes with minimal cross-reactivity between the antigens. Furthermore, quantification performance of fxVFA was tested on 16 serum samples and fxVFA-predicted concentrations had >0.9 coefficients of determination and <15 % coefficients of variation with the respect to a standard ELISA test.
We demonstrate a computational paper-based vertical flow assay (VFA) for point-of-care serodiagnosis of Lyme Disease (LD). We leveraged the multiplexed nature of the VFA and functionalized it using different antigen panels specific to LD. The paper-based VFA operation takes <20min, after which a hand-held reader captures an image of the sensing membrane. A deep learning-based algorithm processes the signals from multiple immunoreactions to output a diagnostic decision (i.e., positive/negative). This cost-effective computational VFA platform achieved a sensitivity and a specificity of 90.5% and 87%, respectively, demonstrating its promising potential for point-of-care diagnosis of LD even in resource-limited settings.
We report a deep-learning based compact spectrometer. Using a spectral encoder chip composed of unique plasmonic tiles (containing periodic nanohole-arrays), diffraction patterns created by the transmitted light through these tiles are captured by a CMOS sensor-array, without the use of any lenses or other components between the plasmonic encoder and the CMOS-chip. A neural network rapidly reconstructs the input light spectrum from the recorded lensless image data, which was blindly tested on randomly-generated new spectra to demonstrate the success of this computational on-chip spectrometer, which will find applications in various fields that demand low-cost and compact spectrum analyzers.
We present a deep learning-based framework to perform single image super-resolution of SEM images. We experimentally demonstrated that this network can enhance the resolution of SEM images by two-fold, allowing for a reduction of the scanning time and electron dosage by four-fold without any significant loss of image quality. Using blindly tested regions of a gold-on-carbon resolution test target, we quantitatively and qualitatively confirmed the image enhancement achieved by the trained network. We believe that this technique has the potential to improve the SEM imaging process, particularly in cases where imaging throughput and minimizing beam damage are of utmost importance.
We report a point-of-care (POC) assay and neural network-based diagnostic algorithm for Lyme Disease (LD). A paper-based test in a vertical flow format detects 16 different IgM and IgG LD-specific antibodies in serum using a mobile phone reader and automated image processing to quantify its colorimetric signals. The multiplexed information is then input into a trained neural-network which infers a positive or negative result for LD. The assay and diagnostic decision algorithm were validated through fully-blinded testing of human serum samples yielding an area-under-the-curve (AUC), sensitivity, and specificity of 0.950, 90.5%, and 87.0% respectively, outperforming previous Lyme POC tests.
We demonstrate a low-cost and rapid paper-based vertical flow assay (VFA) for quantification of C-Reactive Protein (CRP). We use deep learning-based analysis of this VFA and its multiplexed sensing channels to achieve accurate quantification, as well as to overcome fabrication and operational variations along with limitations borne out of the hook effect, validating our results with clinical samples. This computational point-of-care test could be used for stratification of patients into cardiovascular disease risk assessment groups following standard clinical cut-offs. It can also broadly serve as a computational sensing platform for future point-of-care sensing and diagnostic applications.
We demonstrate a contact-lens (CL) based mobile sensing system which can be used to measure protein levels in human tear. By using a cost-effective mobile-phone-based well-plate reader and a fluorescent assay, we quantify lysozyme nonspecifically bound to CLs. We monitored the lysozyme levels of 9 healthy volunteers to establish individual baselines, and then compared these measurements to participants who had been diagnosed with Dry Eye Disease (N=6), observing a statistically significant difference in their means. Due to its non-invasive and simple operation, this method could be used for tear-based sensing and health monitoring applications in point-of-care settings and at home.
Methods of detection for key biomarkers in bodily fluids that are specific, low-cost, and non-invasive are in high demand for various biomedical applications. Specifically, field-portable and cost-effective devices which can enable these measurements to be made at home or in the field are crucial for practical and widespread use of these technologies. Plasmonic sensors form an emerging bio-sensor platform that responds to the specific adsorption of bio-molecules via a spectral change in transmission or reflection mode of operation. However, to read and quantify their spectral response, expensive and bulky optics such as broad-band light sources and high resolution spectrometers are typically employed, severely limiting their potential applications in resource-limited settings. In an effort to build low-cost and compact plasmonic readers, we have developed a computational sensing framework that uses machine learning to statistically differentiate the sensor’s spectral response from fabrication related variability and other noise factors, and select the optimal illumination bands for the lowest-possible read-out error. To validate this framework we constructed a low-cost and field-portable plasmonic reader around the optimal illumination bands selected for different plasmonic nano-hole array designs. We then validated the superior performance of our computational reader by measuring a large number of independently fabricated flexible plasmonic sensors made using scalable, nano-imprint lithography methods without the use of a clean room. Additionally, these structures can subsequently be transfer-printed onto disposable, wearable platforms where they can be chemically modified to specifically and sensitively capture target biomarkers in bio-fluids e.g., tear or sweat, enabling new applications in point-of-care diagnostics.
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