UCLA

Disease diagnosis has long been a basis of modern medicine, enabling early intervention and effective treatment strategies. Recent advancements in nanotechnology have ushered in a new era of diagnostic techniques, with nanoscale bioparticles emerging as powerful tools in this endeavor. Nanoscale bioparticles, including extracellular vesicles, viruses, and other bioactive entities, have gained prominence due to their unique properties that make them ideal candidates for biomarker detection. These tiny structures, often measuring around 100 nanometers, carry a wealth of molecular information reflective of the physiological and pathological states of the body. Their presence, composition, and abundance in biological fluids such as blood, saliva, and urine hold invaluable clues for diagnosing a wide range of diseases This dissertation presents a cutting-edge approach to disease diagnosis by integrating the analysis of nano bioparticles, Surface-Enhanced Raman Spectroscopy (SERS), and machine learning techniques targeting disease diagnosis. SERS, with its unparalleled sensitivity and specificity, serves as a powerful tool for the characterization of biomolecules. We investigate the feasibility of SERS in capturing the intricate spectral signatures of nano bioparticles, revealing valuable insights into their molecular composition. Moreover, machine learning models are harnessed to decipher this wealth of spectral data, enabling the identification of disease-specific biomarkers with unprecedented accuracy. The article encompasses a detailed exploration of exosome biology, the principles of SERS, the intricacies of machine learning based data analysis methodologies applied to spectral data, preliminary achievements in non-small cell lung cancer diagnostic study, and feasibility of identify SARS-CoV-2 biomarkers for COVID detection. We present a particular subgroup of exosomes derived from human bronchial epithelial cells possessing distinct spectral signatures that can be a potential indicator of non-small cell lung cancer early metastasis, and a rapid and accurate SERS based platform for COVID detection using salivary specimen, superior in some cases to RT-PCR and antigen test. The integration of these multidisciplinary approaches represents a significant step toward revolutionizing disease diagnosis through the convergence of nano bioparticle analysis, spectroscopy, and machine learning, offering a promising avenue for early and accurate disease detection in clinical settings.