| Title |
Optical recognition of salivary proteins by use of molecularly imprinted poly(ethylene-co-vinyl alcohol)/quantum dot composite nanoparticles
|
|---|---|
| Published in |
Analytical & Bioanalytical Chemistry, March 2010
|
| DOI | 10.1007/s00216-010-3631-x |
| Pubmed ID | |
| Authors |
Mei-Hwa Lee, Yun-Chao Chen, Min-Hsien Ho, Hung-Yin Lin |
| Abstract |
Molecularly imprinted polymers (MIPs) have long been studied for applications in biomolecule recognition and binding; compared with natural antibodies, they may offer advantages in cost and stability. We report on the development of MIPs that "self-report" concentrations of bound analytes via fluorescence changes in embedded quantum dots (QDots). Composite QDot/MIPs were prepared using phase inversion of poly(ethylene-co-vinyl alcohol) (EVAL) solutions with various ethylene mole ratios in the presence of salivary target molecules (e.g. amylase, lipase, and lysozyme). These major protein components of saliva have been implicated as possible biomarkers for pancreatic cancer. The optimum (highest imprinting effectiveness) ethylene mole ratios of the commercially available EVALs were found to be 32, 38, and 44 mol% for the imprinting of amylase, lipase, and lysozyme, respectively. QD fluorescence quenching was observed on binding of analytes to composite MIPs in a concentration-dependent manner, and was used to construct calibration curves. Finally, the composite MIP particles were used for the quantitative detection of amylase, lipase, and lysozyme in real samples (saliva) and compared with a commercial Architect ci 8200 chemical analysis system. |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 27% |
| Researcher | 8 | 22% |
| Student > Doctoral Student | 4 | 11% |
| Professor > Associate Professor | 2 | 5% |
| Student > Master | 2 | 5% |
| Other | 5 | 14% |
| Unknown | 6 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 15 | 41% |
| Engineering | 4 | 11% |
| Materials Science | 3 | 8% |
| Agricultural and Biological Sciences | 2 | 5% |
| Chemical Engineering | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 8 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 November 2014.
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#22,759,452
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Outputs from Analytical & Bioanalytical Chemistry
#7,542
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#99,210
of 103,652 outputs
Outputs of similar age from Analytical & Bioanalytical Chemistry
#62
of 62 outputs
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