{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:42:11Z","timestamp":1792784531162,"version":"4.5.0"},"reference-count":49,"publisher":"American Chemical Society (ACS)","issue":"36","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"name":"Natural Science Foundation of Shanghai","award":["24ZR1459800"],"award-info":[{"award-number":["24ZR1459800"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["22127806"],"award-info":[{"award-number":["22127806"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["22174022"],"award-info":[{"award-number":["22174022"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["22274028"],"award-info":[{"award-number":["22274028"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Effective screening for colorectal cancer (CRC) remains limited by the invasiveness of colonoscopy and the insufficient sensitivity of conventional liquid biopsy approaches. Here, we present Machine Learning-Assisted, Ago-Mediated Droplet Fluorescence Analysis with a Knowledge-Enabled Agent (MADAK-Agent), an integrated analytical platform that combines an Argonaute-mediated cascade amplification module with a monodisperse droplet-assisted, object-resolved post-amplification fluorescence measurement workflow and a knowledge-enabled interpretation interface. We developed a dual-channel cascade system coupling the exponential amplification reaction (EXPAR) with Clostridium perfringens Argonaute (CpAgo)-mediated cleavage, enabling the conversion of trace circulating miR-21 and miR-92a into distinct fluorescent signals. Following EXPAR\u2013CpAgo cascade amplification, the reaction products were compartmentalized into monodisperse microdroplets using a coaxial flow-focusing microfluidic chip. Droplet-level fluorescence quantification yielded calculated limits of detection (LODs) of 45.6 aM for miR-21 and 59.2 aM for miR-92a. Compared with bulk fluorescence readout, the LODs were improved by 5.20-fold for miR-21 and 3.09-fold for miR-92a. Automated droplet segmentation and feature extraction were achieved using a Cellpose-based analysis module, reducing operator-dependent variability. Based on the extracted droplet features, a support vector machine (SVM) classifier achieved area under the receiver operating characteristic curve (AUC) values of 0.901 for miR-21 and 0.848 for miR-92a in a cohort of 60 clinical serum samples. To enable evidence-grounded interpretation, we further implemented a retrieval-augmented generation (RAG)-based question-answering (QA) agent with dual operating modes. Both the QA agent and the image analysis module are deployable as web-based and local tools, supporting flexible and privacy-preserving use. MADAK-Agent establishes a unified and deployable framework for sensitive droplet-based miRNA detection with integrated analysis, interpretation, and reporting.<\/jats:p>","DOI":"10.1021\/acs.analchem.6c04511","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T10:01:55Z","timestamp":1788775315000},"page":"27156-27170","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Assisted, Ago-Mediated Droplet Fluorescence\nAnalysis with a Knowledge-Enabled Agent for Colorectal Cancer Diagnostics"],"prefix":"10.1021","volume":"98","author":[{"given":"Hanxue","family":"Li","sequence":"first","affiliation":[{"name":"Fudan University , , ,","place":["Shanghai, China, 200433"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Wu","sequence":"additional","affiliation":[{"name":"Fudan University , , ,","place":["Shanghai, China, 200433"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxiao","family":"Tang","sequence":"additional","affiliation":[{"name":"Fudan University , , ,","place":["Shanghai, China, 200433"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanhua","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University , , ,","place":["Shanghai, China, 200062"]},{"name":"Tongji University , , ,","place":["Shanghai, China, 200090"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luhao","family":"Li","sequence":"additional","affiliation":[{"name":"Fudan University , , ,","place":["Shanghai, China, 200433"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jilie","family":"Kong","sequence":"additional","affiliation":[{"name":"Fudan University , , ,","place":["Shanghai, China, 200433"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2281-5935","authenticated-orcid":true,"given":"Hui","family":"Chen","sequence":"additional","affiliation":[{"name":"Fudan University , , ,","place":["Shanghai, China, 200433"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"issue":"7","key":"2026091511065310000_cit1","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/s41588-019-0423-x","article-title":"Quantitative\nEvidence for Early Metastatic\nSeeding in Colorectal Cancer","volume":"51","author":"Hu","year":"2019","journal-title":"Nat. Genet."},{"issue":"3","key":"2026091511065310000_cit2","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer\nStatistics\n2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for\n36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA: Cancer J. Clin."},{"issue":"38","key":"2026091511065310000_cit3","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202506159","article-title":"Single-Droplet Dual-Target Quantification of circRNA Biomarkers for\nColorectal Cancer Screening","volume":"12","author":"Xu","year":"2025","journal-title":"Adv. Sci."},{"key":"2026091511065310000_cit4","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2025.1574586","article-title":"Circulating miRNAs\nas Liquid Biopsy Biomarkers for\nDiagnosis in Patients with Colorectal Cancer: A Systematic Review\nand Meta-Analysis","volume":"16","author":"Schwab","year":"2025","journal-title":"Front. Genet."},{"issue":"1","key":"2026091511065310000_cit5","doi-asserted-by":"publisher","DOI":"10.1038\/s41392-024-02021-w","article-title":"Liquid Biopsy in Cancer: Current\nStatus, Challenges\nand Future Prospects","volume":"9","author":"Ma","year":"2024","journal-title":"Signal Transduction Targeted\nTher."},{"issue":"30","key":"2026091511065310000_cit6","doi-asserted-by":"publisher","first-page":"10513","DOI":"10.1073\/pnas.0804549105","article-title":"Circulating\nMicroRNAs as Stable Blood-Based Markers\nfor Cancer Detection","volume":"105","author":"Mitchell","year":"2008","journal-title":"Proc. Natl. Acad. Sci.\nU.S.A."},{"issue":"12","key":"2026091511065310000_cit7","doi-asserted-by":"publisher","first-page":"5003","DOI":"10.1073\/pnas.1019055108","article-title":"Argonaute2 Complexes\nCarry a Population of Circulating MicroRNAs Independent of vesicles\nin Human Plasma","volume":"108","author":"Arroyo","year":"2011","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"issue":"1","key":"2026091511065310000_cit8","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-024-12528-8","article-title":"Comparison of the Diagnostic Value of Various MicroRNAs\nin Blood for Colorectal Cancer: A Systematic Review and Network Meta-Analysis","volume":"24","author":"Xu","year":"2024","journal-title":"BMC Cancer"},{"issue":"15","key":"2026091511065310000_cit9","doi-asserted-by":"publisher","first-page":"8363","DOI":"10.1111\/jcmm.15282","article-title":"Serum miR-92a-1\nIs a Novel Diagnostic Biomarker for Colorectal Cancer","volume":"24","author":"Shi","year":"2020","journal-title":"J. Cell. Mol. Med."},{"issue":"1","key":"2026091511065310000_cit10","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-83365-z","article-title":"Systematic\nEvaluation of Multiple\nqPCR Platforms, NanoString and miRNA-Seq for microRNA Biomarker Discovery\nin Human Biofluids","volume":"11","author":"Hong","year":"2021","journal-title":"Sci. Rep."},{"key":"2026091511065310000_cit11","doi-asserted-by":"publisher","DOI":"10.1016\/j.trac.2022.116641","article-title":"Isothermal Nucleic\nAcid Amplification for Food Safety Analysis","volume":"153","author":"Xia","year":"2022","journal-title":"TrAC, Trends Anal. Chem."},{"issue":"6","key":"2026091511065310000_cit12","doi-asserted-by":"publisher","DOI":"10.1007\/s13205-023-03628-6","article-title":"Isothermal Nucleic Acid Amplification\nand Its Uses\nin Modern Diagnostic Technologies","volume":"13","author":"Srivastava","year":"2023","journal-title":"3 Biotech"},{"issue":"37","key":"2026091511065310000_cit13","doi-asserted-by":"publisher","first-page":"11856","DOI":"10.1002\/anie.201712217","article-title":"Exponential\nIsothermal Amplification\nof Nucleic Acids and Assays for Proteins, Cells, Small Molecules,\nand Enzyme Activities: An EXPAR Example","volume":"57","author":"Reid","year":"2018","journal-title":"Angew.\nChem., Int. Ed."},{"issue":"16","key":"2026091511065310000_cit14","doi-asserted-by":"publisher","first-page":"4135","DOI":"10.1039\/D4AN00609G","article-title":"EXPAR for Biosensing:\nRecent Developments and Applications","volume":"149","author":"Ou","year":"2024","journal-title":"Analyst"},{"key":"2026091511065310000_cit15","doi-asserted-by":"publisher","DOI":"10.1016\/j.trac.2024.118081","article-title":"Trends in\nthe Use of Argonaute Proteins in Molecular Diagnosis","volume":"183","author":"Li","year":"2025","journal-title":"TrAC, Trends Anal. Chem."},{"issue":"9","key":"2026091511065310000_cit16","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1038\/nsmb.2879","article-title":"The Evolutionary Journey of Argonaute Proteins","volume":"21","author":"Swarts","year":"2014","journal-title":"Nat. Struct. Mol. Biol."},{"issue":"1","key":"2026091511065310000_cit17","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-07449-7","article-title":"DNA Interference\nand Beyond: Structure\nand Functions of Prokaryotic Argonaute Proteins","volume":"9","author":"Lisitskaya","year":"2018","journal-title":"Nat. Commun."},{"issue":"36","key":"2026091511065310000_cit18","doi-asserted-by":"publisher","first-page":"13401","DOI":"10.1021\/acs.analchem.3c01990","article-title":"Programmable Clostridium perfringens Argonaute-Based, One-Pot Assay for the\nMultiplex Detection of miRNAs","volume":"95","author":"Lin","year":"2023","journal-title":"Anal. Chem."},{"issue":"32","key":"2026091511065310000_cit19","doi-asserted-by":"publisher","first-page":"11290","DOI":"10.1021\/acs.analchem.2c01945","article-title":"Programmable\nAnalysis of MicroRNAs by Thermus thermophilus Argonaute-Assisted Exponential\nIsothermal Amplification for Multiplex Detection (TEAM)","volume":"94","author":"Lin","year":"2022","journal-title":"Anal. Chem."},{"key":"2026091511065310000_cit20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2024.159053","article-title":"Exponential Isothermal Amplification-Assisted\nClostridium butyricum Argonaute-Based Biosensor for Homogeneous, Multiplex,\nand Sensitive Detection of Foodborne Pathogens","volume":"505","author":"He","year":"2025","journal-title":"Chem. Eng. J."},{"key":"2026091511065310000_cit21","doi-asserted-by":"publisher","DOI":"10.1016\/j.bios.2022.114377","article-title":"DropCRISPR: A LAMP-Cas12a Based Digital Method for\nUltrasensitive Detection of Nucleic Acid","volume":"211","author":"Wu","year":"2022","journal-title":"Biosens.\nBioelectron."},{"issue":"3","key":"2026091511065310000_cit22","doi-asserted-by":"publisher","DOI":"10.1007\/s10404-017-1889-4","article-title":"From Single-Molecule Detection to\nNext-Generation Sequencing: Microfluidic Droplets for High-Throughput\nNucleic Acid Analysis","volume":"21","author":"Ding","year":"2017","journal-title":"Microfluid. Nanofluid."},{"issue":"5","key":"2026091511065310000_cit23","doi-asserted-by":"publisher","first-page":"2068","DOI":"10.1021\/acs.analchem.3c04426","article-title":"Mesophilic Argonaute-Mediated\nPolydisperse Droplet Biosensor for Amplification-Free, One-Pot, and\nMultiplexed Nucleic Acid Detection Using Deep Learning","volume":"96","author":"Wang","year":"2024","journal-title":"Anal. Chem."},{"key":"2026091511065310000_cit24","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2024.148845","article-title":"A Digital\nPlatform for One-Pot Signal Enhanced Foodborne Pathogen\nDetection Based on Mesophilic Argonaute-Driven Polydisperse Microdroplet\nReactors and Machine Learning","volume":"482","author":"Wen","year":"2024","journal-title":"Chem. Eng. J."},{"issue":"2","key":"2026091511065310000_cit25","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1038\/s43016-024-01082-y","article-title":"Multiplexed\nFood-Borne Pathogen Detection Using an Argonaute-Mediated Digital\nSensor Based on a Magnetic-Bead-Assisted Imaging Transcoding System","volume":"6","author":"Wang","year":"2025","journal-title":"Nat. Food"},{"key":"2026091511065310000_cit26","doi-asserted-by":"publisher","DOI":"10.1038\/s43586-023-00212-3","article-title":"Droplet-Based Microfluidics","volume":"3","author":"Moragues","year":"2023","journal-title":"Nat. Rev. Methods Primers"},{"issue":"58","key":"2026091511065310000_cit27","doi-asserted-by":"publisher","DOI":"10.3791\/3437","article-title":"Fluorescence Detection Methods for\nMicrofluidic Droplet Platforms","author":"Solvas","year":"2011","journal-title":"J. Visualized\nExp."},{"issue":"20","key":"2026091511065310000_cit28","doi-asserted-by":"publisher","first-page":"3437","DOI":"10.1039\/C7LC00826K","article-title":"Image-Based\nFeedback and Analysis System for Digital Microfluidics","volume":"17","author":"Vo","year":"2017","journal-title":"Lab Chip"},{"issue":"2","key":"2026091511065310000_cit29","doi-asserted-by":"publisher","first-page":"1003","DOI":"10.1021\/ac202578x","article-title":"Evaluation\nof a Droplet Digital Polymerase\nChain Reaction Format for DNA Copy Number Quantification","volume":"84","author":"Pinheiro","year":"2012","journal-title":"Anal. Chem."},{"issue":"27","key":"2026091511065310000_cit30","doi-asserted-by":"publisher","first-page":"3410","DOI":"10.1039\/C9AY01005J","article-title":"A Novel Method Based\non a Mask R-CNN\nModel for Processing dPCR Images","volume":"11","author":"Hu","year":"2019","journal-title":"Anal. Methods"},{"key":"2026091511065310000_cit31","doi-asserted-by":"crossref","unstructured":"He, K.; Gkioxari, G.; Dolla\u0301r, P.; Girshick, R. In  Mask R-CNN, Proceedings of the IEEE International Conference on Computer Vision\n(ICCV); IEEE: Venice,\nItaly, 2017; pp 2961\u20132969.","DOI":"10.1109\/ICCV.2017.322"},{"issue":"1","key":"2026091511065310000_cit32","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1038\/s41592-020-01018-x","article-title":"Cellpose:\nA Generalist Algorithm for Cellular Segmentation","volume":"18","author":"Stringer","year":"2021","journal-title":"Nat. Methods"},{"issue":"3","key":"2026091511065310000_cit33","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1038\/s41592-025-02595-5","article-title":"Cellpose3: One-Click Image Restoration\nfor Improved\nCellular Segmentation","volume":"22","author":"Stringer","year":"2025","journal-title":"Nat. Methods"},{"issue":"8","key":"2026091511065310000_cit34","doi-asserted-by":"publisher","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","article-title":"Large Language\nModels in Medicine","volume":"29","author":"Thirunavukarasu","year":"2023","journal-title":"Nat. Med."},{"key":"2026091511065310000_cit35","doi-asserted-by":"crossref","unstructured":"Trivedi, H.; Balasubramanian, N.; Khot, T.; Sabharwal, A. In  Interleaving Retrieval with Chain-of-Thought\nReasoning for Knowledge-Intensive Multi-Step Questions, Proceedings\nof the 61st Annual Meeting of the Association for Computational Linguistics\n(Vol. 1: Long Papers); Association for Computational\nLinguistics: Toronto, Canada, 2023; pp 10014\u201310037.","DOI":"10.18653\/v1\/2023.acl-long.557"},{"key":"2026091511065310000_cit36","doi-asserted-by":"crossref","unstructured":"Zhang, Z.; Zhang, X.; Ren, Y.; Shi, S.; Han, M.; Wu, Y.; Lai, R.; Cao, Z. In  IAG: Induction-Augmented\nGeneration Framework for Answering Reasoning Questions, Proceedings\nof the Conference on Empirical Methods in Natural Language Processing; Association for Computational Linguistics: Singapore, 2023; pp 1\u201314.","DOI":"10.18653\/v1\/2023.emnlp-main.1"},{"key":"2026091511065310000_cit37","doi-asserted-by":"crossref","unstructured":"Ammann, P. J. L.; Golde, J.; Akbik, A. In  Question Decomposition for Retrieval-Augmented\nGeneration, Proceedings of the 63rd Annual Meeting of the\nAssociation for Computational Linguistics (Vol. 4: Student Research\nWorkshop); Association for Computational Linguistics: Vienna, Austria, 2025; pp 497\u2013507.","DOI":"10.18653\/v1\/2025.acl-srw.32"},{"issue":"8","key":"2026091511065310000_cit38","doi-asserted-by":"publisher","first-page":"4643","DOI":"10.1021\/acs.analchem.4c06677","article-title":"ChatExosome: An Artificial\nIntelligence (AI) Agent Based on Deep Learning of Exosomes Spectroscopy\nfor Hepatocellular Carcinoma (HCC) Diagnosis","volume":"97","author":"Yang","year":"2025","journal-title":"Anal. Chem."},{"issue":"8103","key":"2026091511065310000_cit39","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1038\/s41586-025-10072-4","article-title":"Synthesizing\nScientific Literature\nwith Retrieval-Augmented Language Models","volume":"650","author":"Asai","year":"2026","journal-title":"Nature"},{"issue":"2","key":"2026091511065310000_cit40","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/s41551-025-01463-z","article-title":"CRISPR-GPT\nfor Agentic Automation of Gene-Editing Experiments","volume":"10","author":"Qu","year":"2026","journal-title":"Nat. Biomed. Eng."},{"key":"2026091511065310000_cit41","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","article-title":"Microsoft COCO:\nCommon Objects\nin Context","volume-title":"Computer Vision \u2013 ECCV\n2014","author":"Lin","year":"2014"},{"issue":"1","key":"2026091511065310000_cit42","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1021\/acs.analchem.4c05145","article-title":"Multiplex Digital Nucleic Acid Analysis\nby a LAMP\u2013Argonaute Coupling Assay via a Parallel Droplet Fusion\nSlipChip","volume":"97","author":"Luo","year":"2025","journal-title":"Anal. Chem."},{"issue":"4","key":"2026091511065310000_cit43","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aay5952","article-title":"Isothermal Digital\nDetection of MicroRNAs\nUsing Background-Free Molecular Circuit","volume":"6","author":"Gines","year":"2020","journal-title":"Sci.\nAdv."},{"issue":"9","key":"2026091511065310000_cit44","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202105450","article-title":"Deep-dLAMP:\nDeep Learning-Enabled Polydisperse Emulsion-Based Digital Loop-Mediated\nIsothermal Amplification","volume":"9","author":"Chen","year":"2022","journal-title":"Adv. Sci."},{"key":"2026091511065310000_cit45","unstructured":"Wang, W.; Wei, F.; Dong, L.; Bao, H.; Yang, N.; Zhou, M. In  MiniLM: Deep Self-Attention Distillation for Task-Agnostic\nCompression\nof Pre-Trained Transformers, Advances in Neural Information\nProcessing Systems, Larochelle, H.; Ranzato, M.; Hadsell, G.; Balcan, M. F.; Lin, H., Eds.; Curran Associates, Inc., 2020; pp 5776\u20135788."},{"key":"2026091511065310000_cit46","doi-asserted-by":"crossref","unstructured":"Reimers, N.; Gurevych, I. In  Sentence-BERT:\nSentence\nEmbeddings Using Siamese BERT-Networks, Proceedings of the\n2019 Conference on Empirical Methods in Natural Language Processing\nand the 9th International Joint Conference on Natural Language Processing\n(EMNLP-IJCNLP); Association for Computational Linguistics: Hong Kong, China, 2019; pp 3982\u20133992, 10.18653\/v1\/d19-1410.","DOI":"10.18653\/v1\/D19-1410"},{"key":"2026091511065310000_cit47","unstructured":"Abid, A.; Abdalla, A.; Abid, A.; Khan, D.; Alfozan, A.; Zou, J.\n          Gradio: Hassle-Free Sharing and Testing of ML Models\nin the Wild, arXiv:1906.02569. arXiv.org e-Print archive, 2019https:\/\/arxiv.org\/abs\/1906.02569."},{"key":"2026091511065310000_cit48","doi-asserted-by":"publisher","DOI":"10.2196\/75262","article-title":"Development and Evaluation\nof a Retrieval-Augmented Generation Chatbot for Orthopedic and Trauma\nSurgery Patient Education: Mixed-Methods Study","volume":"4","author":"Baur","year":"2025","journal-title":"JMIR AI"},{"issue":"2","key":"2026091511065310000_cit49","doi-asserted-by":"publisher","DOI":"10.1056\/AIoa2300068","article-title":"Almanac\u2500Retrieval-Augmented\nLanguage Models for Clinical Medicine","volume":"1","author":"Zakka","year":"2024","journal-title":"NEJM\nAI"}],"container-title":["Analytical\nChemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.analchem.6c04511","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/ancham\/article-pdf\/98\/36\/27156\/68401287\/acs.analchem.6c04511.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/ancham\/article-pdf\/98\/36\/27156\/68401287\/acs.analchem.6c04511.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T15:52:44Z","timestamp":1789487564000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/ancham\/article\/98\/36\/27156\/5422237\/Machine-Learning-Assisted-Ago-Mediated-Droplet"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,7]]},"references-count":49,"journal-issue":{"issue":"36","published-online":{"date-parts":[[2026,9,7]]},"published-print":{"date-parts":[[2026,9,15]]}},"URL":"https:\/\/doi.org\/10.1021\/acs.analchem.6c04511","relation":{},"ISSN":["0003-2700","1520-6882"],"issn-type":[{"value":"0003-2700","type":"print"},{"value":"1520-6882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,7]]}}}