Photonic Crystal-Enhanced Fluorescence Imaging Immunoassay for Cardiovascular Disease Biomarker Screening with Machine Learning Analysis.

When myocardial walls experience stress due to cardiovascular diseases, like heart failure, hormone N-terminal pro-B-type natriuretic peptide (NT-proBNP) is secreted into the blood. Early detection of NT-proBNP can assist diagnosis of heart failure and enable early medical intervention. A simple, cost-effective detection technique such as the widely used fluorescence imaging immunoassay is yet to be developed to detect clinically relevant levels of NT-proBNP. In this work, we demonstrate photonic crystal-enhanced fluorescence imaging immunoassay using diatom biosilica, which is capable of detecting low levels of NT-proBNP in solution with the concentration range of 0~100 pg/mL. By analyzing the fluorescence images in the spatial and spatial frequency domain with principle component analysis (PCA) and partial least squares regression (PLSR) algorithms, we create a predictive model that achieves great linearity with a validation R value of 0.86 and a predictive root mean square error of 14.47, allowing for good analyte quantification. To demonstrate the potential of the fluorescence immunoassay biosensor for clinical usage, we conducted qualitative screening of high and low concentrations of NT-proBNP in human plasma. A more advanced machine learning algorithm, the support vector machine classification, was paired with the PCA and trained by 160 fluorescence images. In the 40 testing images, we achieved excellent specificity of 93%, as well as decent accuracy and sensitivity of 78% and 65% respectively. Therefore, the photonic crystal-enhanced fluorescence imaging immunoassay reported in this article is feasible to screen clinically relevant levels of NT-proBNP in body fluid and evaluate the risk of heart failure.