[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119807-en":3,"doc-seo-119807-105":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},119807,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Identification of fluorescently-barcoded nanoparticles using machine learning","Fluorescent barcoding supports multiplexing by distinguishing multiple targets in complex mixtures, but nanoparticle small size lowers signal and increases heterogeneity, making reliable barcode separation difficult for many nano-carrier bioassays. This work presents a machine-learning-assisted workflow for writing, reading, and classifying barcoded PLGA–PEG nanoparticles at single-particle level. It includes encapsulating fluorescent markers without altering physicochemical properties, optimizing confocal imaging, and using a machine-learning barcode reader. Results show heterogeneity as a key challenge and demonstrate that nanoscale information from dye environments (e.g., FRET) improves identification, with guidance for balancing barcode number and classification accuracy across bioassays.","Identification of fluorescently-barcoded nanoparticles using machine learning  \nCitation for published version (APA):  \nOrtiz-Perez, A. , Izquierdo-Lozano, C. , Meijers, R. , Grisoni, F. , & Albertazzi, L. (2023) . Identification of fluorescently-barcoded nanoparticles using machine learning. Nanoscale Advances, 5(8), 2307-2317. [https://doi.org/10.1039/d2na00648k](https://doi.org/10.1039/d2na00648k)  \nDocument license:  \nCC BY  \nDOI:  \n10.1039/d2na00648k  \nDocument status and date:  \nPublished: 21/04/2023  \nDocument Version:  \nPublisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers)  \nPlease check the document version of this publication:  \n• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.  \n• The final author version and the galley proof are versions of the publication after peer review.  \n• The final published version features the final layout of the paper including the volume, issue and page numbers.  \nLink to publication  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal.  \nIf the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license above, please follow below link for the End User Agreement:  \n[www.tue.nl/taverne](www.tue.nl/taverne)  \nTake down policy  \nIf you believe that this document breaches copyright please contact us at:  \n[openaccess@tue.nl](openaccess@tue.nl)  \nproviding details and we will investigate your claim.  \nDownload date: 04. Oct. 2023  \nOpen Access Article . Pu on 23 Marc 2023. Down on 5/3/2023blished h loaded 12:53:02 PM .  \nhicle is licensed under a Creative C mmons A 3 0 U d[ttr .](ttr .)ibution npor e nce.  \nNanoscale Advances  \nPAPER  \nView Article Online View Journal | View Issue  \nCite this: Nanoscale Adv., 2023, 5, 2307  \nReceived 21st September 2022  \nAccepted 8th March 2023 DOI: 10.1039/d2na00648k[rsc.li/nanoscale-advances](rsc.li/nanoscale-advances)  \nIdentification of fluorescently-barcoded nanoparticles using machine learning†  \nAna Ortiz-Perez,  ‡ Cristina Izquierdo-Lozano,  ‡ Rens Meijers, Francesca Grisoni  and Lorenzo Albertazzi  *  \nBarcoding of nano-and micro-particles allows distinguishing multiple targets at the same time within a complex mixture and is emerging as a powerful tool to increase the throughput of many assays. Fluorescent barcoding is one of the most used strategies, where microparticles are labeled with dyesand classified based on fluorescence color, intensity, or other features. Microparticles are ideal targets due to their relative ease of detection, manufacturing, and higher homogeneity. Barcoding is considerably more challenging in the case of nanoparticles (NPs), where their small size results ina lower signal and greater heterogeneity. This is a significant limitation since many bioassays require the use of nano-sized carriers. In this study, we introduce a machine-learning-assisted workflow to write, read, and classify barcoded PLGA–PEG NPs at a single-particle level. This procedure is based on the encapsulation of fluorescent markers without modifying their physicochemical properties (writing), t","cbCaiml6gPJJZ4iZ","https://ap.wps.com/l/cbCaiml6gPJJZ4iZ","pdf",1286694,1,12,"English","en",105,"# Introduction\n## High-throughput particle-based assays and barcoding\n## Optical fluorescent barcoding and its limitations for nanoparticles\n# Machine-learning-assisted workflow\n## Writing barcodes in PLGA–PEG nanoparticles\n## Reading via optimized confocal imaging\n## Classifying with a machine-learning barcode reader\n# Key findings and guidance\n## Role of nanoparticle heterogeneity in barcode separation\n## Using nanoscale dye information (FRET) for identification\n## Trade-off between multiplexing and classification accuracy","[{\"question\":\"What problem does the study address in fluorescent barcoding of nanoparticles?\",\"answer\":\"Nanoparticles produce lower fluorescence signals and show higher heterogeneity than microparticles, which makes barcode separation and classification challenging for multiplex bioassays.\"},{\"question\":\"What does the proposed machine-learning-assisted workflow include?\",\"answer\":\"It covers writing barcodes by encapsulating fluorescent markers without changing their physicochemical properties, reading by optimizing confocal imaging, and classifying using a machine-learning-based barcode reader.\"},{\"question\":\"How does the study improve barcode identification accuracy?\",\"answer\":\"It identifies nanoparticle heterogeneity as a main obstacle and shows that nanoscale information from dye environments, such as FRET-derived effects, can aid barcode identification.\"}]","Identification of fluorescently-barcoded nanoparticles using machine learning | 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problem does the study address in fluorescent barcoding of nanoparticles?","Question",{"text":75,"@type":76},"Nanoparticles produce lower fluorescence signals and show higher heterogeneity than microparticles, which makes barcode separation and classification challenging for multiplex bioassays.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the proposed machine-learning-assisted workflow include?",{"text":80,"@type":76},"It covers writing barcodes by encapsulating fluorescent markers without changing their physicochemical properties, reading by optimizing confocal imaging, and classifying using a machine-learning-based barcode reader.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study improve barcode identification accuracy?",{"text":84,"@type":76},"It identifies nanoparticle heterogeneity as a main obstacle and shows that nanoscale information from dye environments, such as FRET-derived effects, can aid barcode 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