A Wi-Fi-enabled, automated device for pathogen detection via LAMP and paper-based colorimetric assay

Abstract

Rapid and reliable detection of Vibrio vulnificus, a life-threatening bacterial pathogen, is critical for timely clinical intervention. Here, we report a fully automated, portable nucleic acid detection device that integrates a mechanical slide-based loop-mediated isothermal amplification (LAMP) detection system (S-LAMP) with a qualitative test paper for colorimetric detection. The system is controlled by a LabVIEW-programmed NI myRIO microprocessor, enabling automated sample handling, DNA extraction, isothermal amplification, and fuchsin-based colorimetry within a single enclosed workflow. A key innovation is a wax-encapsulated microfluidic reaction chamber, in which wax serves as a thermally responsive seal to isolate the DNA extraction zone and subsequently melt at the LAMP operating temperature to initiate reagent–template interaction. The device employs fuchsin-based colorimetry, where positive samples containing the target DNA appear red, while negative samples remain colorless. The integrated Wi-Fi module allows real-time remote monitoring of temperature settings and operational status, enhancing field-deployable diagnostics. Under optimized conditions, the LAMP assay achieved a detection limit of 102 CFU per mL within 20 minutes at 65 °C. This lab-on-a-chip platform streamlines the nucleic acid testing workflow, minimizes the risk of contamination, and reduces reagent consumption. Therefore, it is a promising tool for pathogen surveillance and point-of-care diagnostic applications in clinical diagnosis.

Graphical abstract: A Wi-Fi-enabled, automated device for pathogen detection via LAMP and paper-based colorimetric assay

Supplementary files

Article information

Article type
Paper
Submitted
23 Dec 2025
Accepted
09 Feb 2026
First published
10 Feb 2026

Anal. Methods, 2026, Advance Article

A Wi-Fi-enabled, automated device for pathogen detection via LAMP and paper-based colorimetric assay

Y. T. Lai, N. Y. Lee and J. J. Chen, Anal. Methods, 2026, Advance Article , DOI: 10.1039/D5AY02131F

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