Machine Learning-Assisted, Ago-Mediated Droplet Fluorescence Analysis with a Knowledge-Enabled Agent for Colorectal...

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-CpAgo 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.