[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120479-en":3,"doc-seo-120479-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120479,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Science and Technology of Extracellular Vesicles in Medical Applications Utilizing SERS and Machine Learning","Extracellular vesicles (EVs), especially exosomes, offer major potential for biomedical diagnostics and therapeutics because of their rich biochemical composition. Precise characterization of individual exosomes remains difficult with conventional analytical workflows. This dissertation investigates Surface Enhanced Raman Spectroscopy (SERS) paired with advanced machine learning to robustly analyze single exosomes and extract deeper biochemical information. It develops SERS-based biophysical interpretation, integrates neural networks for high-dimensional spectral challenges, predicts protein composition, and improves SERS throughput via plasmonic precipitation and functionalization, validated in clinically relevant gastric-cancer diagnosis and ocular therapy contexts.","UCLA  \nUCLA Electronic Theses and Dissertations  \nTitle  \nScience and Technology of Extracellular Vesicles in Medical Applications Utilizing SERS and Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/1mf14459](https://escholarship.org/uc/item/1mf14459)  \nISBN  \n9798293839667  \nAuthor  \nSrivastava, Siddharth  \nPublication Date  \n2025-09-12  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nLos Angeles  \nScience and Technology of Extracellular Vesicles  \nin Medical Applications Utilizing SERS and Machine Learning  \nA dissertation submitted in partial satisfaction of the  \nrequirements for the degree Doctor of Philosophy  \nin Materials Science and Engineering  \nby  \nSiddharth Srivastava  \n2025  \n© Copyright by  \nSiddharth Srivastava  \n2025  \nABSTRACT OF THE DISSERTATION  \nScience and Technology of Extracellular Vesicles  \nin Medical Applications Utilizing SERS and Machine Learning  \nby  \nSiddharth Srivastava  \nDoctor of Philosophy in Materials Science and Engineering  \nUniversity of California, Los Angeles, 2025  \nProfessor Ya-Hong Xie, Chair  \nExtracellular vesicles (EVs), particularly exosomes, show significant promise in biomedical diagnostics and therapeutics due to their rich biochemical content. However, precisely characterizing individual exosomes remains challenging for conventional analytical methods. This thesis explores Surface Enhanced Raman Spectroscopy (SERS) combined with advanced machine learning techniques to robustly analyze individual exosomes and provide deeper biochemical insights.  \nThe thesis begins by detailing the fundamental principles and physics underlying SERS, emphasizing its sensitivity and specificity for biomolecular diagnostics. It addresses practical  \nconsiderations in spectral analysis and the challenges involved in accurately interpreting complex, high dimensional SERS data biochemically. The research highlights machine learning integration, particularly neural networks, for effectively overcoming these analytical hurdles. Additionally, the biochemical relevance of SERS signals is explored, where deep learning models were used to predict protein compositions from amino acid SERS profiles.  \nNext, this work focuses on improving on the challenge of the throughput, which significantly restricts the practical application of SERS. This thesis tackles this challenge through innovative approaches, including plasmonic precipitation and surface functionalization strategies. We experimentally validate the potential of this improved platform in clinically relevant scenarios. For instance, early diagnosis of gastric cancer using exosomes derived from tissue and plasma and exploring therapeutic efficacy in ocular treatments using exosomes derived from eyes.  \nThis research advances the integration of SERS and machine learning, providing practical pathways toward diagnostic and therapeutic applications.  \nThe dissertation of Siddharth Srivastava is approved.  \nXimin He  \nAaswath Pattabhi Raman  \nSophie X. Deng  \nYa-Hong Xie, Committee Chair  \nUniversity of California, Los Angeles  \n2025  \nTo my parents, my sister and my partner, whose love and support made everything achievable.  \nContents  \n1. Introduction ............................................................................................................................. 1  \n1.1 Exosomes as Diagnostic and Therapeutic Biomarkers................................................................ 1  \n1.2 Limitations of Conventional Exosome Analysis Techniques...................................................... 3  \n1.3 Surface-Enhanced Raman Spectroscopy (SERS) for Single-Exosome Analysis........................ 5  \n1.4 Summary and Thesis Objective ................................................................................................... 9  \n1.5 References ....................................................","cbCaicLEN0ciJjEN","https://ap.wps.com/l/cbCaicLEN0ciJjEN","pdf",6789531,1,188,"English","en",105,"# Introduction\n## Exosomes as Diagnostic and Therapeutic Biomarkers\n## Limitations of Conventional Exosome Analysis Techniques\n## Surface-Enhanced Raman Spectroscopy (SERS) for Single-Exosome Analysis\n## Summary and Thesis Objective\n## References\n# SERS and Machine Learning: Methodology for Extracting Biochemically Relevant Information\n## Physics of SERS\n## Exploring Light-Matter Interaction in Surface Enhanced Raman Spectroscopy","[{\"question\":\"Why are extracellular vesicles, especially exosomes, important for medical applications?\",\"answer\":\"They carry rich biochemical content that supports promising diagnostic and therapeutic uses. However, analyzing individual exosomes precisely is still challenging with standard methods.\"},{\"question\":\"How does this thesis use SERS together with machine learning?\",\"answer\":\"It combines SERS with advanced machine learning, including neural networks, to handle complex high-dimensional spectral data. The approach aims to produce robust biochemical insights from individual exosome measurements.\"},{\"question\":\"What strategies does the thesis propose to improve SERS throughput and practical usability?\",\"answer\":\"It addresses throughput limitations using innovative approaches such as plasmonic precipitation and surface functionalization. The improved platform is experimentally validated in clinically relevant scenarios.\"}]","Science and Technology of Extracellular Vesicles in Medical Applications Utilizing SERS and Machine Learning | PDF",1785730293,474,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"science-and-technology-of-extracellular-vesicles-in-medical-applications-utilizing-sers-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/science-and-technology-of-extracellular-vesicles-in-medical-applications-utilizing-sers-and-machine-learning/120479/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are extracellular vesicles, especially exosomes, important for medical applications?","Question",{"text":75,"@type":76},"They carry rich biochemical content that supports promising diagnostic and therapeutic uses. However, analyzing individual exosomes precisely is still challenging with standard methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does this thesis use SERS together with machine learning?",{"text":80,"@type":76},"It combines SERS with advanced machine learning, including neural networks, to handle complex high-dimensional spectral data. The approach aims to produce robust biochemical insights from individual exosome measurements.",{"name":82,"@type":73,"acceptedAnswer":83},"What strategies does the thesis propose to improve SERS throughput and practical usability?",{"text":84,"@type":76},"It addresses throughput limitations using innovative approaches such as plasmonic precipitation and surface functionalization. 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