Sijie Yin , Yanghe Liu , Xinyi Yang , Nasifu Lubanga , Ping Tai , Mengqiu Xiong , Boyue Fan , Xincheng Yang , Zhenlin Nie , Qingsong Zhang , Bangshun He
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引用次数: 0
Abstract
Background
Helicobacter pylori (H. pylori) infects over 50% of the global population and is a significant risk factor for gastric cancer. The pathogenicity of H. pylori is primarily attributed to virulence factors such as vacA. Timely and accurate identification, along with genotyping of H. pylori virulence genes, are essential for effective clinical management and controlling its prevalence.
Methods
In this study, we developed a dual-target RAA-LFD assay for the rapid, visual detection of H. pylori genes (16s rRNA, ureA, vacA m1/m2), using recombinase aided amplification (RAA) combined with lateral flow dipstick (LFD) methods. Both 16s rRNA and ureA were selected as identification genes to ensure reliable detection accuracy.
Results
A RAA-LFD assay was developed to achieve dual-target amplification at a stable 37 °C within 20 min, followed by visualization using the lateral flow dipstick (LFD). The whole process, from amplification to results, took less than 30 min. The 95 % limit of detection (LOD) for 16 s rRNA and ureA, vacA m1, vacA m2 were determined as 3.8 × 10-2 ng/μL, 5.8 × 10-2 ng/μL and 1.4 × 10-2 ng/μL, respectively. No cross-reaction was observed in the detection of common pathogens including Escherichia coli, Klebsiella pneumoniae, Enterococcus faecalis, Staphylococcus aureus, Pseudomonas aeruginosa, and Bacillus subtilis, showing the assay’s high specificity. In the evaluation of the clinical performance of the RAA-LFD assay. A total of 44 gastric juice samples were analyzed, immunofluorescence staining (IFS) and quantitative polymerase chain reaction (qPCR) were used as reference methods. The RAA-LFD results for the 16s rRNA and ureA genes showed complete agreement with qPCR findings, accurately identifying H. pylori infection as confirmed by IFS in 10 out of the 44 patients. Furthermore, the assay successfully genotyped vacA m1/m2 among the positive samples, showing complete agreement with qPCR results and achieving a kappa (κ) value of 1.00.
Conclusion
The dual-target RAA-LFD assay developed in this study provides a rapid and reliable method for detecting and genotyping H. pylori within 30 min, minimizing dependency on sophisticated laboratory equipment and specialized personnel. Clinical validation confirms its efficacy as a promising tool for effectively control of its prevalence and aiding in the precise treatment of H. pylori-associated diseases.
期刊介绍:
The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC)
Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells.
The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.