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生物医学工程评论综述™
SJR: 0.26 SNIP: 0.375 CiteScore™: 1.4

ISSN 打印: 0278-940X
ISSN 在线: 1943-619X

生物医学工程评论综述™

DOI: 10.1615/CritRevBiomedEng.2019026426
pages 217-234

An Experimental Platform for Characterizing Cancer Biomarkers with Capabilities in Noninvasive and Continuous Screening

Mackenzie M. Honikel
School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona
Jeffrey T. La Belle
School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona

ABSTRACT

Early detection is crucial to the proper and effective treatment of two metastatic cancers, prostate cancer and small cell lung cancer. Currently, preventative screenings for these conditions are restricted to high-risk populations and extremely expensive. The discovery of clinically indicative biomarkers has been revolutionary in advancing screening and diagnostic capabilities. Prostate-specific antigen (PSA), an extracellular secreted protein of the prostate gland, and neuron-specific enolase (NSE), an enzyme of neuronal origin, have reported reputable specificity for prostate cancer and small cell lung cancer (SCLC). Current efforts are underway to develop a rapid, label-free means of measuring both PSA and NSE levels in a clinical environment for early screening applications of highly metastatic cancers. Electrochemical impedance spectroscopy (EIS) and impedance time (Z-t) are rapid, sensitive electrochemical techniques previously validated in the detection of several clinically relevant biomarkers, including cardiovascular disease and diabetes mellitus. Herein, we determine the optimal frequencies of PSA (81.38 Hz) and NSE (14.36 Hz) using EIS that are robust across analytical platforms and in the presence of potentially interfering species. The reported empirical evidence supports the prevalence of electrostatic interactions in electrochemical systems and provides alternative theoretical support of previous findings. Finally, Z-t was implemented for its utility in continuous monitoring applications and to lay the foundation for future improvements to continuous sensor platforms.


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