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Cited by 719

The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.

Electrochemical Immunosensors for Detection of Cancer Protein Biomarkers

Open access
Chikkaveeraiah, Bhaskara V., Bhirde, Ashwinkumar A., Morgan, Nicole Y., Eden, Henry S., Chen, Xiaoyuan·2012·ACS Nano

Abstract

Bioanalytical methods have experienced unprecedented growth in recent years, driven in large part by the need for faster, more sensitive, more portable ("point of care") systems to detect protein biomarkers for clinical diagnosis. Electrochemical detection strategies, used in conjunction with immunosensors, offer advantages because they are fast, simple, and low cost. Recent developments in electrochemical immunosensors have significantly improved the sensitivity needed to detect low concentrations of biomarkers present in early stages of cancer. Moreover, the coupling of electrochemical devices with nanomaterials, such as gold nanoparticles, carbon nanotubes, magnetic particles, and quantum dots, offers multiplexing capability for simultaneous measurements of multiple cancer biomarkers. This review will discuss recent advances in the development of electrochemical immunosensors for the next generation of cancer diagnostics, with an emphasis on opportunities for further improvement in cancer diagnostics and treatment monitoring. Details will be given for strategies to increase sensitivity through multilabel amplification, coupled with high densities of capture molecules on sensor surfaces. Such sensors are capable of detecting a wide range of protein quantities, from nanogram to femtogram (depending on the protein biomarkers of interest), in a single sample.

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Metadata

Authors
Chikkaveeraiah, B. V., Bhirde, A. A., Morgan, N. Y., Eden, H. S., & Chen, X.
Venue
ACS Nano
Published
2012-08-06
Volume
6
Issue
8
Pages
6546-6561
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W1983333664
Cited by
719
References
77
FWCI
17.66
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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