3D-printed electrochemical loop-mediated isothermal amplification (E-LAMP): A miniaturized and portable platform to detect methicillin-resistant Staphylococcus aureus (MRSA).

Background: The increasing prevalence of methicillin-resistant Staphylococcus aureus (MRSA), particularly due to the presence of the mecA gene, emphasizes the need for decentralized, rapid, and accurate molecular diagnostics. While qPCR remains the gold standard method, its dependence on expensive equipment and centralized labs limits accessibility in field or point-of-care (POC) settings. To address this limitation, we developed an Electrochemical Loop-Mediated Isothermal Amplification (E-LAMP) platform for rapid, low-cost, and highly sensitive detection of the mecA gene, using 3D-printed electrodes and a smartphone-controlled potentiostat.

Results: The system couples LAMP with electrochemical detection via cresol red, a pH-sensitive dye that undergoes a peak potential shift upon proton release during DNA amplification. Linear sweep voltammetry (LSV) was used to monitor these changes, enabling quantitative analysis. The platform achieved a limit of detection (LOD) of 11 copies μL, which is 30 times more sensitive than conventional colorimetric LAMP. The device submitted to UV-laser surface treatment, enhancing electrode conductivity and response reproducibility. The assay produced results in under 31 min and showed 100 % agreement with qPCR across 10 real MRSA-positive and negative samples, with no cross-reactivity toward non-target bacteria. Quantification of environmental MRSA samples demonstrated strong correlation with qPCR (R = 0.98), and ROC analysis yielded an AUC of 1.0, confirming outstanding sensitivity and specificity.

Significance: E-LAMP offers a powerful alternative for MRSA detection, integration molecular specificity with electrochemical sensitivity in a fully portable, and user-friendly platform. The integration of 3D printing, pH-responsive electrochemical readout, and smartphone interfacing enables low-cost diagnostics in resource-limited settings. This method shows high promise for wider application in infectious disease surveillance, particularly where traditional diagnostics are impractical.