The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Automatic smartphone-based microfluidic biosensor system at the point of care
Abstract
Point-of-care testing technique is increasingly important for healthcare management in human being's daily life. However, traditional biosensor systems for health care are relatively expensive, bulky and hard-to-handle, which largely limits their use in point of care testing. The problems mentioned above are successfully addressed with the popularization of smartphone and the development of microfluidic technology for their applications of biosensor, which integrates smartphones, microfluidic components and sensory elements together, paving the way for wide application of smartphone-based microfluidic biomedical sensory system. According to the varieties of analytes, the most common sensing modalities of biosensor systems are divided into imaging analysis to detect cells and bacteria, biochemical analysis to detect blood sugar and blood fat, immunoassay to detect protein specifically bound to antibody, as well as molecular diagnosis to detect DNA and other biomolecules. Based on the most common analytical methods, this review article covers five types of smartphone-based microfluidic biosensor systems at the point-of-care detection, i.e., smartphone-based imaging biosensor, smartphone-based biochemical sensor, smartphone-based immune biosensor, smartphone-based hybrid biosensor with more than one sensing modality, and smartphone-based molecular sensor. We lay emphasis on reviewing the structures, analytical methods and sensing modalities about the four kinds of biosensor systems with detailed discussions on their application potentials, aiming at giving the audience an overview of the recent developments of automatic smartphone-based microfluidic biosensor systems, as well as their future prospective.
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Cited by
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Metadata
- Authors
- Xu, D., Huang, X., Guo, J., & Ma, X.
- Venue
- Biosensors and Bioelectronics
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 110
- Pages
- 78-88
- ISSN
- 0956-5663 (ISSN-L)
- OpenAlex
- W2791328049
- Cited by
- 264
- References
- 47
- FWCI
- 14.16
- Type
- journal article
- Language
- en
- Open access
- closed
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