Issue 29, 2025

Proof of concept for visual detection of the Helicobacter pylori CagA gene using gold nanoparticle-assisted LAMP and freezing methods

Abstract

Helicobacter pylori is an infectious bacterium causing sores and inflammation in the stomach or duodenum and may also lead to gastric cancer. CagA-positive strains are generally considered more virulent and are associated with a higher risk of developing severe gastric diseases. Besides current detection modalities, non-invasive, sensitive, and early detection of H. pylori is paramount for preventing associated diseases. Loop-mediated isothermal amplification (LAMP) represents a molecular diagnostic technique characterized by its isothermal reaction conditions and enhanced sensitivity. In addition, nanoparticles are extensively used for the diagnosis of pathogens. This study combines the LAMP method with gold nanoparticles (AuNP), namely AuNP–LAMP, for the colorimetric detection of the H. pylori CagA gene. Additionally, the freezing method used in the preparation of the AuNP–CagA conjugate speeds up the preparation process to within 15 minutes. The assay was tested on the CagA gene (443 bp)-containing synthetic plasmid and stool DNA samples. The assay enables visual (colorimetric) identification, obviating the need for expensive detection modalities. The inclusion of AuNPs eliminates the need for pH-dependent dyes and possible mis-amplification, thus improving the sensitivity of the assay. This could enable the detection of H. pylori in samples with low bacterial loads, such as stool, saliva, and gastric juice.

Graphical abstract: Proof of concept for visual detection of the Helicobacter pylori CagA gene using gold nanoparticle-assisted LAMP and freezing methods

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Article information

Article type
Paper
Submitted
28 févr. 2025
Accepted
01 juil. 2025
First published
14 juil. 2025

Anal. Methods, 2025,17, 6107-6116

Proof of concept for visual detection of the Helicobacter pylori CagA gene using gold nanoparticle-assisted LAMP and freezing methods

H. Tombuloglu, Anal. Methods, 2025, 17, 6107 DOI: 10.1039/D5AY00339C

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