[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-331893-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-331893-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","3d-superclusters-with-hybrid-bioinks-for-early-detection-in-breast-cancer","3D Superclusters with Hybrid Bioinks for Early Detection in Breast Cancer","","The surface-enhanced Raman scattering (SERS) method is highlighted for disease diagnosis owing to its ultrahigh sensitivity and the practical need for platforms that are simple to manufacture and consume low amounts of analyte. A strategy is demonstrated that combines deep learning with a flexible, bottom-up three-dimensional (3D) plasmonic cluster SERS platform for early breast cancer detection. The 3D plasmonic cluster (3D-PC) achieves Raman intensity enhancement with detection down to 10−6 M for p-nitrophenol, then uses cancer and healthy subjects’ plasma to fabricate bioinks and build 3D-PC structures. Collected SERS spectra are classified into cancer and healthy clusters with up to 93% accuracy, supporting the platform’s potential for early detection.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/3d-superclusters-with-hybrid-bioinks-for-early-detection-in-breast-cancer/331893/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/3d-superclusters-with-hybrid-bioinks-for-early-detection-in-breast-cancer/331893.png","ImageObject",300,407,{"name":42,"@type":43},"Patrick","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-10","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",15,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why is SERS useful for disease diagnosis in this work?","Question",{"text":63,"@type":64},"SERS provides ultrahigh sensitivity, enabling detection suited to practical diagnostic demands. The study emphasizes benefits like simplicity in manufacturing and low analyte consumption for complex diagnoses.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does the study construct the 3D plasmonic cluster platform?",{"text":68,"@type":64},"It uses a bottom-up strategy to build a flexible and simple 3D plasmonic cluster SERS platform. Deep learning is integrated to classify subtle molecular vibrational fingerprints.",{"name":70,"@type":61,"acceptedAnswer":71},"What classification performance was achieved for breast cancer detection?",{"text":72,"@type":64},"SERS spectra derived from plasma samples were classified into two groups—cancer subjects and healthy subjects—with accuracy up to 93%.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},331893,1790128214,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":116,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145,"preview_page_count":4},549758146520,"https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470","This article is licensed under CC-BY-NC-ND 4.0  \n[pubs.acs.org/acssensors](pubs.acs.org/acssensors)  Article   \n3D Superclusters with Hybrid Bioinks for Early Detection in Breast Cancer  \nThanh Mien Nguyen,∇ SinSung Jeong,∇ Seok Kyung Kang,∇ Seung-Wook Han, Thu M. T. Nguyen, Seungju Lee, Youn Joo Jung, You Hwan Kim, Sunwoo Park, Gyeong-Ha Bak, Young-Chai Ko, Eun-Jung Choi, * Hyun Yul Kim, * and Jin-Woo Oh *  \n Cite This: ACS Sens. 2024, 9, 699−707  \nRead Online  \n\n|  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- |\n| ACCESS   | Metrics & More |  |  Article Recommendations |  | *sı Supporting Information |\n\nABSTRACT: The surface-enhanced Raman scattering (SERS) technique has garnered significant interest due to its ultrahigh sensitivity, making it suitable for addressing the growing demand for disease diagnosis. In addition to its sensitivity and uniformity, an ideal SERS platform should possess characteristics such as simplicity in manufacturing and low analyte consumption, enabling practical applications in complex diagnoses including cancer. Furthermore, the integration of machine learning algorithms with SERScan enhance the practical usability of sensing devices by effectively classifying the subtle vibrational fingerprints produced by molecules such as those found in human blood. In this study, we demonstrate an approach for early detection of breast cancer using a bottom-up strategy to construct a flexible and simple three-dimensional (3D) plasmonic cluster SERS platform integrated with a deep learning algorithm. With these advantages of the 3D plasmonic cluster, we demonstrate that the 3D plasmonic cluster (3D-PC)  \nexhibits a significantly enhanced Raman intensity through detection limit down to 10−6 M (femtomole-(10−17 mol)) for p-nitrophenol (PNP) molecules. Afterward, the plasma of cancer subjects and healthy subjects was used to fabricate the bioink to build 3D-PC structures. The collected SERS successfully classified into two clusters of cancer subjects and healthy subjects with high accuracy of up to 93%. These results highlight the potential of the 3D plasmonic cluster SERS platform for early breast cancer detection and open promising avenues for future research in this field.  \nKEYWORDS: breast cancer diagnosis, SERS, 3D plasmonic cluster, machine learning, blood sensor  \nN opnlainvsmaasiveand cancerserum idseatecctionommounsingtest huthmatagebsloodesseniaal biochemical information about the body in clinical diagnosis.1 Proteins and deoxyribonucleic acid (DNA) in human blood derived from the death of normal blood cells in healthy individuals.2 However, the processes of apoptosis and necrosis of cells in cancer patients result in changes in biomolecules in proteins and DNA.3 It is referred to as tumor-related biomarkers, and it provides a signal for cancer diagnosis in  \nearly detection, which aids effective treatment planning.  \nBreast cancer is a leading cause of death among women worldwide, accounting for 24.2% of cancer cases and with a fatality rate of 15%. The incidence of breast cancer has been increasing rapidly in recent years, possibly due to environmental pollution and changes in food supplies.4−8 Despite significant improvements in prognosis through high rates of breast cancer screening, the mortality rate remains high due to delays in early detection. Therefore, early identification, treatment, and monitoring play crucial roles in improving breast cancer outcomes.9, 10 Mammography is commonly employed as a screening method for breast cancer. However, it faces limitations when it comes to detecting breast cancer in many Asian women who have dense breasts.5 Therefore,  \n© 2024 The Authors. Published by American Chemical Society  \nsupplementary procedures such as breast ultrasound and core needle biopsy are often necessary to aid in the detection process. Unfortunately, the requirement for these additional procedures can result in delays in the early detection of breast cancer.  \nSERS is a highly ","cbCaijoYQgBm2dqG","https://ap.wps.com/l/cbCaijoYQgBm2dqG","pdf",7078273,"English","# Abstract\n## System overview and rationale\n## 3D plasmonic cluster SERS performance\n## Bioink fabrication from plasma samples\n## Classification results and implications","[{\"question\":\"Why is SERS useful for disease diagnosis in this work?\",\"answer\":\"SERS provides ultrahigh sensitivity, enabling detection suited to practical diagnostic demands. The study emphasizes benefits like simplicity in manufacturing and low analyte consumption for complex diagnoses.\"},{\"question\":\"How does the study construct the 3D plasmonic cluster platform?\",\"answer\":\"It uses a bottom-up strategy to build a flexible and simple 3D plasmonic cluster SERS platform. Deep learning is integrated to classify subtle molecular vibrational fingerprints.\"},{\"question\":\"What classification performance was achieved for breast cancer detection?\",\"answer\":\"SERS spectra derived from plasma samples were classified into two groups—cancer subjects and healthy subjects—with accuracy up to 93%.\"}]","3D Superclusters with Hybrid Bioinks for Early Detection in Breast Cancer | PDF",1789995641,23]