Date of Award
2025
Degree Type
Thesis
Degree Name
Master of Science in Interdisciplinary Neurosciences
Department
Interdisciplinary Neuroscience
First Advisor
Daniel Roxbury
Abstract
Single-walled carbon nanotubes (SWCNTs) possess unique physicochemical and optical properties that make them ideal candidates for biomedical imaging, biosensing, and disease diagnostics. This thesis explores the potential of SWCNT-based spectral fingerprinting combined with ML (Machine Learning) algorithms to optimize bioimaging, disease detection and prediction, with a specific focus on differentiating between healthy and amyotrophic lateral sclerosis (ALS) lymphoblastic patient samples. By functionalizing SWCNTs with single-stranded DNA, we enhance their stability and target specificity, enabling their application in serum patient samples.
A comprehensive spectral analysis of DNA-SWCNTs was conducted using near-infrared fluorescence spectroscopy and other characterization techniques, including UV-Vis-absorption spectroscopy. The spectral data obtained from healthy and ALS serum samples were processed using mathematical approaches, statistic methods and advanced ML algorithms to establish a nanotube spectral fingerprint, facilitating precise disease characterization and classification.
This work highlights the advantage of integrating SWCNT-based nanotechnology with computational methodologies to develop a novel, non-invasive specific diagnostic tool. The study demonstrates high sensitivity and specificity in differentiating healthy and ALS patient serum lymphoblast samples, paving the way for enhanced early-stage neurological diagnostics. Furthermore, the findings contribute to the broader of nano biosensors and their applications in personalized medicine and point of care diagnostics.
The successful implementation of this approach could revolutionize bioimaging and sensing techniques, providing a rapid, reliable, and cost-effective platform for early disease detection and monitoring. Future research will focus on refining the SWCNT functionalization by expanding the sensors to different DNA strands, varying the processing of data and ML models, expanding the sample size, and investigating additional biomarker features to further validate and test the technique’s clinical applicability for different neurological diseases.
Recommended Citation
Monroy Lopez, Rodrigo, "NANOTUBE SPECTRAL FINGERPRINTING AND MACHINE LEARNING FOR OPTIMIZED BIOIMAGING/SENSING AND DISEASE DETECTION APPLICATIONS IN ALS" (2025). Open Access Master's Theses. Paper 2595.
https://digitalcommons.uri.edu/theses/2595