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Cited by 175

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Using Commercially Available Personal Glucose Meters for Portable Quantification of DNA

Open access
Xiang, Yu, Lu, Yi·2012·Analytical Chemistry

Abstract

DNA detection is commonly used in molecular biology, pathogen analysis, genetic disorder diagnosis, and forensic tests. While traditional methods for DNA detection such as polymerase chain reaction (PCR) and DNA microarrays have been well developed, they require sophisticated equipment and operations, and thus it is still challenging to develop a portable and quantitative DNA detection method for the public use at home or in the field. Although many other techniques and devices have been reported to make the DNA detection simple and portable, very few of them are currently accessible to the public for quantitative DNA detection because of either the requirement of laboratory-based instrument or lack of quantitative detection. Herein we report application of personal glucose meters (PGMs), which are widely available, low cost, and simple to use, for quantitative detection of DNA, including a hepatitis B virus DNA fragment. The quantification is based on target-dependent binding of cDNA-invertase conjugate with the analyte DNA, thereby transforming the concentration of DNA in the sample into glucose through invertase-catalyzed hydrolysis of sucrose. Instead of amplifying DNA strands through PCR, which is vulnerable to contaminations commonly encountered for home and field usage, we demonstrate here signal amplifications based on enzymatic turnovers, making it possible to detect 40 pM DNA using PGM that can detect glucose only at the mM level. The method also shows excellent selectivity toward single nucleotide mismatches.

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Metadata

Authors
Xiang, Y., & Lu, Y.
Venue
Analytical Chemistry
Published
2012-02-06
Volume
84
Issue
4
Pages
1975-1980
ISSN
  • 0003-2700 (ISSN-L)
  • 1520-6882
OpenAlex
W2111939469
Cited by
175
References
5
FWCI
6.74
Type
journal article
Language
en
Open access
green

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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