Lyme disease (LD) is a tick-borne illness and can lead to severe health complications if left undiagnosed/untreated. This study introduces a novel approach to point-of-care testing for LD, utilizing deep-learning-enabled peptide-based serodiagnosis. 82 unique patient serum samples were used for validation, obtained from the Bay Area Lyme Disease Biobank and the CDC Lyme Serum Repository. Blinded test results demonstrated outstanding diagnostic performance, with 84.0% sensitivity and 96.3% specificity. By integrating deep learning techniques with a peptide-based sensing panel, this serodiagnosis offers a rapid (< 15 minutes) and cost-effective (< $0.5/test) platform for LD diagnostics, while minimizing cross-reactivity.
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.
Lyme disease (LD) is a tick-borne illness caused by the bacterium Borrelia burgdorferi, which can cause severe symptoms if untreated. We present a novel diagnostic platform utilizing synthetic peptides and a deep-learning-based analytical algorithm to detect LD-specific antibodies in patient serum samples. Blinded samples acquired from the Centers for Disease Control and Prevention (CDC) were tested using our platform, achieving a sensitivity of 95% among disseminated disease and a specificity of 100% across all healthy endemic controls and cross-infected samples. Our peptide-based assay offers high sensitivity, specificity, ease-of-use, and cost-effectiveness, making it an attractive platform for point-of-care LD diagnosis.
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 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.
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