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- In relation to this article, we declare that there is no conflict of interest.
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Received October 19, 2017
Accepted November 20, 2017
- This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Strategies for the optimization of bead-immunoassays for the effective detection of target biomolecules
Jaewoo Son
Jangsun Hwang
Dohyun Lee
Muhammad Saad Khan
Yeonho Jo
Kyungwoo Lee
Chanhwi Park
Sachin Chavan
Youngmin Seo1
Younghyun Choi
Sangsoo Kim
Dasom Kim
Dokyun Na†
Jonghoon Choi†
School of Integrative Engineering, Chung-Ang University, Seoul 06974, Korea 1Center for Biomaterials, Boimedical Research Institute, Korea Institute of Science and Technology, Seoul 02792, Korea
nanomed@cau.ac.kr
Korean Journal of Chemical Engineering, March 2018, 35(3), 805-811(7), 10.1007/s11814-017-0323-7
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Abstract
Immunoassays are analytical methods using antibody-specific reactions to analyze samples. Due to recent developments in antibody technology, the scope of potential samples has expanded to not only proteins, but also low molecular-weight compounds, carbohydrates, lipids, and microorganisms. Immunoassays have the advantage of being highly sensitive, capable of detecting small amounts, and thus have potential for application in biosensors. Immunoassays using magnetic beads have been developed and can be converted to more diverse platforms than the existing limited well plate-based assay. Furthermore, magnetic bead immunoassays detect analytical samples more quickly, and are becoming one of the most suitable immunoassay tools applicable to biosensors. However, their development requires optimization for the improvement of detection ability for specific samples. Therefore, we propose a guideline for solving detection problems occurring in magnetic bead immunoassay optimization processes. It is aimed to be a good reference, enabling researchers performing such optimization more quickly and efficiently.
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