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Issue 21, 2015
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Biomarker detection for disease diagnosis using cost-effective microfluidic platforms

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Abstract

Early and timely detection of disease biomarkers can prevent the spread of infectious diseases, and drastically decrease the death rate of people suffering from different diseases such as cancer and infectious diseases. Because conventional diagnostic methods have limited application in low-resource settings due to the use of bulky and expensive instrumentation, simple and low-cost point-of-care diagnostic devices for timely and early biomarker diagnosis is the need of the hour, especially in rural areas and developing nations. The microfluidics technology possesses remarkable features for simple, low-cost, and rapid disease diagnosis. There have been significant advances in the development of microfluidic platforms for biomarker detection of diseases. This article reviews recent advances in biomarker detection using cost-effective microfluidic devices for disease diagnosis, with the emphasis on infectious disease and cancer diagnosis in low-resource settings. This review first introduces different microfluidic platforms (e.g. polymer and paper-based microfluidics) used for disease diagnosis, with a brief description of their common fabrication techniques. Then, it highlights various detection strategies for disease biomarker detection using microfluidic platforms, including colorimetric, fluorescence, chemiluminescence, electrochemiluminescence (ECL), and electrochemical detection. Finally, it discusses the current limitations of microfluidic devices for disease biomarker detection and future prospects.

Graphical abstract: Biomarker detection for disease diagnosis using cost-effective microfluidic platforms

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Article information


Submitted
21 Apr 2015
Accepted
23 Jun 2015
First published
23 Jun 2015

Analyst, 2015,140, 7062-7081
Article type
Critical Review
Author version available

Biomarker detection for disease diagnosis using cost-effective microfluidic platforms

S. T. Sanjay, G. Fu, M. Dou, F. Xu, R. Liu, H. Qi and X. Li, Analyst, 2015, 140, 7062
DOI: 10.1039/C5AN00780A

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