Abstract
Quantum well defects (QWDs) on single-walled carbon nanotubes (SWCNTs) enable expanded analyte sensitization and functionalization for biosensing applications. We are using these capabilities to develop new research and diagnostic methods for cancers, infectious disease, and other indications. For instance, we are addressing issues with serum biomarker measurements, which are widely used for diagnostics, but these markers often fail to result in a clear diagnosis of complex disease indications. We developed a method using QWD-modified SWCNTs (QWNTs) to identify a “spectral fingerprint” through the collection of large data sets of molecular binding interactions to an array of QWNTs. We found that a library of QWNTs exhibit differentiated spectral variations in response to an ensemble of molecular binding events in patient serum. Via machine learning algorithms, we built prediction models of nanosensor responses that can reliably identify ovarian cancer, and we have expanded this approach to other indications without known biomarkers, providing a general method to identify clinical indications.
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