A multi-institutional research team analyzed spectral signatures of ovarian cancer by harnessing machine learning and the fluorescence of carbon nanotubes.
Lehigh University's Yoona Yang said the perception-based nanosensor platform "consists of a sensing array that captures a certain feature of the analytes [biomarkers] in a specific way, and then the ensemble response from the array is analyzed by the computational perceptive model. It can detect various analytes at once, which makes it much more efficient."
Single-wall carbon nanotubes wrapped in DNA composed the array, whose diverse surfaces drew proteins within a uterine lavage sample impregnated with ovarian cancer biomarkers.
The researchers trained an algorithm with spectral signature data from the nanotube emissions to identify patterns indicating each biomarker's presence and concentration.
From Lehigh University P.C. Rossin College of Engineering and Applies Science
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