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DOI 10.1016/j.jim.2025.114000

Медицинская библиотека · Journal of immunological methods · 2025

Development of a sensitive sandwich ELISA for the envelope domain III protein of dengue virus type 2.

Ma Ziting, Su Jingyi, Han Zelin, Ren Ximing, Guo Jinnian, Jiang Lu, Zhao Suqing

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Аннотация

Dengue, caused by the dengue virus (DENV), is a rapidly spreading infectious disease that poses a significant global health challenge. Rapid and accurate diagnosis is critical for effective disease management and epidemic control, as dengue is a kind of vector-borne disease and lacks definitive medical treatments. Current antibodies and antigens for DENV immunoassay, including immunoglobulin G (IgG), immunoglobulin M (IgM), and non-structural protein 1 (NS1), have drawbacks like cross-reaction and false-negative results in the late stages of primary infection or secondary infection. Envelope domain III (EDIII) protein, which is immunogenic and is a recognition region and binding site for DENV receptors, can also be the potential detection target of DENV infection. However, few studies of EDIII protein as a detection target have been reported. Therefore, this study developed a sensitive and specific ELISA for DENV-2_EDIII protein detection. Specific polyclonal antibodies (pAbs) against the DENV-2_EDIII protein were generated by immunizing the New Zealand White rabbits with the recombinant DENV-2_EDIII protein. Using these pAbs, a highly sensitive and specific ELISA method was developed. The assay demonstrated a wide detection range (7.8 ng/mL to 500 ng/mL) and a low limit of detection (LOD) of 0.156 ng/mL and showed no cross-reactivity with other orthoflaviviruses. These findings may provide a new methodological framework and research direction for the diagnosis of dengue after further clinical verifications.

Для цитирования: Ma Ziting, Su Jingyi, Han Zelin, Ren Ximing, Guo Jinnian, Jiang Lu, Zhao Suqing. Development of a sensitive sandwich ELISA for the envelope domain III protein of dengue virus type 2.. Journal of immunological methods. 2025. DOI: 10.1016/j.jim.2025.114000.

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