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DOI 10.1016/j.mimet.2026.107587

Медицинская библиотека · Journal of microbiological methods · 2026

Visual detection of five common vaginitis pathogens by multiplex recombinase polymerase amplification combined with lateral flow strips.

Zhou Siyi, Hu Tingting, Xing Yuqing, Bi Huijuan, Jiang Min, Shen Jilu

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Аннотация

Vaginitis frequently poses serious threats to women's health. If left untreated, it can increase the risk of infection by other pathogens such as HPV, leading to cervical dysplasia. This study developed a rapid detection method based on multiplex recombinase polymerase amplification (mRPA) combined with lateral flow strips (LFS) for the simultaneous identification of five common vaginitis pathogens: Neisseria gonorrhoeae, Gardnerella vaginalis, Candida albicans, Ureaplasma urealyticum, and Trichomonas vaginalis. The novelty of this technology lies in the fact that, the 5' end of the RPA primer was modified by a nucleic acid tagged (NAT) sequence to complementarily pair with the LFS capture probe. The entire testing process can be completed within 50-55 min and provides visual results. Furthermore, the lowest detection limits of the platform for Neisseria gonorrhoeae, Gardnerella vaginalis, Candida albicans, Ureaplasma urealyticum, and Trichomonas vaginalis respectively were 1.5 × 10 CFU/mL, 1.5 × 102 CFU/mL, 1.5 × 10 CFU/mL, 1.35 × 102 copies/μL, and 1.02 × 102 copies/μL, and there was no nucleic acid cross-reactivity with other pathogens. Clinical validation using 122 samples showed a sensitivity of 97.1%, specificity of 90%, and accuracy of 95.9% compared to conventional culture and microscopy. This mRPA-LFS platform is rapid, specific, and sensitive, showing promise as an effective point-of-care testing (POCT) tool.

Для цитирования: Zhou Siyi, Hu Tingting, Xing Yuqing, Bi Huijuan, Jiang Min, Shen Jilu. Visual detection of five common vaginitis pathogens by multiplex recombinase polymerase amplification combined with lateral flow strips.. Journal of microbiological methods. 2026. DOI: 10.1016/j.mimet.2026.107587.

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