An electrochemical sensor platform for rapid nucleic acid detection of Influenza A in unamplified samples and machine-learning-assisted infection diagnosis

Influenza A remains a major cause of respiratory infection that demands for rapid, accurate and readily accessible diagnostic tools for timely clinical decision-making and outbreak control. Here, we report a smartphone-assisted electrochemical biosensor platform for amplification-free nucleic acid detection of Influenza A based on magnetic beads enriched target capture and methylene blue (MB)-mediated signal transduction. Under optimized conditions, the platform enabled sensitive detection of both synthetic complementary DNA and RNA, with limits of detection of 0.33aM and 3.32aM, respectively, while maintaining high selectivity against mismatched and non-target sequences. Additionally, the platform is also applied to the detection of RNA extracted from nasopharyngeal swab samples and shows superior discrimination ability between positive and negative samples as well as strong correlation with reverse transcription quantitative polymerase chain reaction (RT-qPCR) results. Finally, an optimal machine-learning framework based on a support vector classifier (SVC) is developed to analyze DPV signal profiles and directly classify clinical samples without explicit comparison to blank-current responses, achieving an accuracy of 100% in the clinical cohort. Unlike conventional nucleic acid detection approaches that mainly rely on predefined thresholds or quantitative signal interpretation, this study establishes a machine-learning-assisted classification strategy for direct discrimination of influenza A nucleic-acid positive and negative samples based on electrochemical DPV signal profiles. This simple algorithmic framework is particularly suitable for portable devices with resource constrained microcontrollers to realize rapid result analysis. Our results shed a light on potential portable platform for decentralized rapid nucleic acid testing and promising technique for point-of-care infectious disease diagnostics.