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      Bifunctional magnetic nanobeads for sensitive detection of avian influenza A (H7N9) virus based on immunomagnetic separation and enzyme-induced metallization

      , , , , , ,
      Biosensors and Bioelectronics
      Elsevier BV

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          Abstract

          Bifunctional magnetic nanobeads (bi-MBs) were fabricated by co-immobilizing target recognition molecules and signal molecules on a magnetic nanobead surface, which were used as both separation and enrichment carriers and signal carriers. The bi-MBs could capture and separate avian influenza A (H7N9) virus (H7N9 AIV) from complex samples efficiently based on the specific reaction between antigen-antibody and their good magnetic response, which simplified sample pretreatment and saved the detection time. Taking advantages of their high surface to volume ratio and rich surface functional groups, multiple alkaline phosphatase (ALP) signal molecules were tethered on the surface of bi-MBs which greatly amplified the detection signal. As an efficient signal amplification strategy, enzyme-induced metallization had been integrated with bi-MBs and anodic stripping voltammetry to construct an ultrasensitive electrochemical immunosensor for H7N9 AIV detection. Under the optimal conditions, the introduction of bi-MBs could amplify the detection signal in about four times compared with the same immunoassay without MBs, and the method showed a wide linear range of 0.01-20 ng/mL with a detection limit of 6.8 pg/mL. The electrochemical immunosensor provides a simple and reliable platform with high sensitivity and selectivity which shows great potential in early diagnosis of diseases.

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          Author and article information

          Journal
          Biosensors and Bioelectronics
          Biosensors and Bioelectronics
          Elsevier BV
          09565663
          June 2015
          June 2015
          : 68
          : 586-592
          Article
          10.1016/j.bios.2015.01.051
          25643598
          4d2cf22e-3215-4ecb-b74f-6bf9e2562338
          © 2015

          https://www.elsevier.com/tdm/userlicense/1.0/

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