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Carbon nanotubes micro-arrays: characterization and application in biosensing of free proteins and label-free capture of breast cancer cells

"Circulating tumor cells (CTCs) are cells released into the bloodstream from primary tumors and are suspected to be one of the main causes behind metastatic spreading of cancer. The ability to capture and analyze circulating tumor cells in clinical samples is of great interest in prevailing patient prognosis and clinical management of cancer. Carbon nanotubes, individual rolled-up graphene sheets, have emerged as exciting materials for probing the biomolecular interactions. With diameter of about 1 nm, they can attach themselves to cell surface receptors through specific antibodies and hold a great potential for diagnostic cellular profiling. Carbon nanotubes can be either semiconducting or metallic, and the electronic properties of either type rivals the best known materials. Small size of nanotubes and the ability to functionalize their surface using 1-Pyrenebutanoic Acid, Succinimidyl Ester (PASE), enables a versatile probe for developing a platform for capture and analysis of cancer biomarkers and circulating tumor cells. Although nanotubes have previously been used to electrically detect a variety of molecules and proteins, here for the first time we demonstrate the label free capture of spiked breast cancer cells using ultra-thin carbon nanotube film micro-array devices in a drop of buffy coat and blood. A new statistical approach of using Dynamic Time Warping (DTW) was used to classify the electrical signatures with 90% sensitivity and 90% specificity in blood. These results suggest such label free devices could potentially be useful for clinical capture and further analysis of circulating tumor cells. This thesis will go in-depth the properties of carbon nanotubes, device fabrication and characterization methodologies, functionalization protocols, and experiments in buffy coats and in blood. Combination of nano and biological materials, functionalization protocols and advanced statistical classifiers can potentially enable clinical translation of such devices in the future. "

Identiferoai:union.ndltd.org:wpi.edu/oai:digitalcommons.wpi.edu:etd-dissertations-1346
Date16 August 2016
CreatorsKhosravi, Farhad
ContributorsBalaji Panchapakesan, Advisor, Christopher R. Lambert, Committee Member, Yuxiang Shawn Liu, Committee Member, Pratap M. Rao, Committee Member, Shesh N. Rai, Committee Member, Mark W. Richman, Committee Member
PublisherDigital WPI
Source SetsWorcester Polytechnic Institute
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceDoctoral Dissertations (All Dissertations, All Years)

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