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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Biosensors and Bioel...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Biosensors and Bioelectronics
Article . 2017 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Detection of low-abundance biomarker lipocalin 1 for diabetic retinopathy using optoelectrokinetic bead-based immunosensing

Authors: Jhih-Cheng, Wang; Hu-Yao, Ku; Tain-Song, Chen; Han-Sheng, Chuang;

Detection of low-abundance biomarker lipocalin 1 for diabetic retinopathy using optoelectrokinetic bead-based immunosensing

Abstract

Early diagnosis of diabetic retinopathy (DR) is vital but challenging. DR is a common complication and a major cause of vision loss in patients with diabetes mellitus. Without appropriate medical intervention, visual impairment may become a great burden to our healthcare system. In clinical practice, the current diagnostic methods, such as fluorescence angiography and optical coherence tomography, remain constrained by non-quantitative examinations and individual ophthalmologists' experiences. Late diagnosis often prevents early treatment. To address the constraints on current diagnostics, this study developed an optoelectrokinetic bead-based immunosensing technique for detecting lipocalin 1 (LCN1), a DR biomarker. The concentration level of LCN1 in the tears of DR patients increases with DR severity. The immunoassay was dependent on the formation of sandwiched immunocomplexes on the particles. A secondary antibody labeled with dyes/quantum dots (QDs) was used to visualize the presence of the target antigens. Rapid electrokinetic patterning (REP), an optoelectrokinetic technique, was used to dynamically enhance the fluorescent signal by concentrating the modified particles. The limit of detection (LOD) of the technique could reach 110pg/mL. Only 1.5μL of a sample fluid was required for the measurement. Our results showed that highly sensitive and improved LOD is subjected to particle stacking, small particle size, and compact cluster. By labeling different particle sizes with dyes/QDs for LCN1 and TNF-α, we successfully used REP to detect the two DR biomarkers on the same platform. The development of an optoelectrokinetic bead-based immunosensing technique can provide new insights into diagnosing other low-abundance diseases in the future.

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Keywords

Immunoassay, Diabetic Retinopathy, Tumor Necrosis Factor-alpha, Optical Devices, Biosensing Techniques, Equipment Design, Electromagnetic Fields, Tears, Quantum Dots, Animals, Humans, Coloring Agents, Antibodies, Immobilized, Biomarkers, Lipocalin 1

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
33
Top 10%
Top 10%
Top 10%
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