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Repurposing Old Antibodies for New Diseases by Exploiting Cross-Reactivity and Multicolored Nanoparticles

机译:通过利用交叉反应性和多彩多姿的纳米粒子来重新抑制新疾病的旧抗体

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摘要

We exploit the cross-reactivity of dengue (DENV) and Zika (ZIKV) virus polyclonal antibodies for nonstructural protein 1 (NS1) to construct a selective sensor that can detect yellow fever virus (YFV) NS1 in a manner similar to chemical olfaction. DENV and ZIKV antibodies were screened for their ability to bind to DENV, ZIKV, and YFV NS1 by enzyme linked immunosorbent assay (ELISA) and in pairs in paper immunoassays. A strategic arrangement of antibodies immobilized on paper and conjugated to different colored gold NPs was used to distinguish the three biomarkers. Machine learning of test area RGB values showed that with two spots, readout accuracies of 100% and 87% were obtained for both pure NS1and DENV/YFV mixtures, respectively. Additional image preprocessing allowed differentiation between all four DENV serotypes with 92% accuracy. The technique was extended to hack a commercial DENV test to detect YFV and ZIKV by augmentation with DENV and ZIKV polyclonal antibodies.
机译:我们利用登革热(DENV)和ZIKA(ZIKV)病毒多克隆抗体的非结构蛋白1(NS1)的交叉反应性,以构建可以类似于化学嗅觉的方式检测黄热病毒(YFV)NS1的选择性传感器。 筛选DenV和Zikv抗体以通过酶联免疫吸附测定(ELISA)与DenV,Zikv和YFV NS1结合的能力和在纸张免疫测定中成对。 使用在纸上固定并与不同颜色的金NPS缀合的抗体的战略布置用于区分三种生物标志物。 测试区域的机器学习RGB值显示,对于纯NS1和Denv / YFV混合物,分别获得了100%和87%的读出精度为100%和87%。 额外的图像预处理的所有四个DENV血清型之间的允许差异,精度为92%。 该技术扩展以破解商业DenV测试以通过使用DenV和Zikv多克隆抗体进行增强来检测YFV和ZIKV。

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