Photonic crystal-enhanced fluorescence imaging immunoassay for cardiovascular disease biomarker screening with machine learning analysis

SENSORS AND ACTUATORS B-CHEMICAL

卷:290

Article

Squire, Kenneth J.¹  Zhao, Yong^1,2  Tan, Ailing^1,3  Sivashanmugan, Kundan¹  Kraai, Joseph A.⁴  Rorrer, Gregory L.⁴  Wang, Alan X.¹ 

[1] Oregon State Univ, Sch Elect Engn & Comp Sci, 1148 Kelley Engn Ctr, Corvallis, OR 97331 USA

[2] Yanshan Univ, Sch Elect Engn, Key Lab Measurement Technol & Instrumentat Hebei, Qinhuangdao 066004, Hebei, Peoples R China

[3] Yanshan Univ, Sch Informat Sci & Engn, Key Lab Special Fiber & Fiber Sensor Hebei Prov, Qinhuangdao 066004, Hebei, Peoples R China

[4] Oregon State Univ, Sch Chem Biol & Environm Engn, 116 Johnson Hall, Corvallis, OR 97331 USA

关键词: Fluorescence imaging;    Photonic crystals;    Immunoassay;    Cardiovascular biomarker;    Machine learning;   

DOI  :  10.1016/j.snb.2019.03.102

来源: Elsevier

【 摘 要 】

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(similar to) 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-2 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.

【 授权许可】

Free