High Fidelity Machine-Learning-Assisted False Positive Discrimination in Loop-Mediated Isothermal Amplification Using Nanopore-Based Sizing and Counting.

Loop-mediated isothermal amplification (LAMP) is a rapid, sensitive, and cost-effective method for developing point-of-care nucleic acid testing due to its isothermal nature. Yet, LAMP can suffer from the issue of false positives, which can compromise the specificity of the results. LAMP false positives typically arise due to contamination, nonspecific amplification, and nonspecific signal reporting (intercalating dyes, colorimetric, turbidity, etc.). While dye-labeled primers or probes have been introduced for multiplexed detection and enhanced specificity in LAMP assays, they carry the risk of reaction inhibition. This inhibition can result from the labeled primers with fluorophores or quenchers and probes that do not fully dissociate during reaction. This work demonstrated a nanopore-based system for probe-free LAMP readouts by employing amplicon sizing and counting, analogous to an electronic version of gel electrophoresis. We first developed a model to explore LAMP kinetics and verified distinct patterns between true and false positives via gel electrophoresis. Subsequently, we implemented nanopore sized counting and calibrated the event charge deficit (ECD) values and frequencies to ensure a fair analysis of amplicon profiles. This sized counting method, integrated with machine learning, achieved 91.67% accuracy for false positive discrimination, enhancing LAMP's reliability for nucleic acid detection.