Issue 9, 2017

A simple functional carbon nanotube fiber for in vivo monitoring of NO in a rat brain following cerebral ischemia

Abstract

Nitric oxide (NO) is key free-radical messenger and neuronal signal which is closely related to many brain diseases. It is a challenge to develop a sensitive and reliable biosensor for in vivo monitoring of NO in the brain. In this research, a simple ratiometric electrochemical biosensor for NO monitoring in rat brain following cerebral ischemia was developed using a carbon nanotube fiber (CNF) modified with hemin, in which the CNF not only served as a platform to assemble the hemin molecule, but greatly facilitated the electron transfer of hemin on to the electrode surface. Additionally, the hemin molecule was found to play dual roles: as a stable catalyst for the reduction of NO for selective detection of NO at −0.67 V versus silver/silver chloride, as well as an inner reference element to provide a built-in correction, to avoid the interference from the complicated brain environment. The developed ratiometric biosensor can detect NO with a linear range from 25 to 1000 nM, with a low limit of detection down to 10 nM, which fulfills the requirements for in vivo measurement of NO. The remarkable analytical performance of the present biosensor, as well as the long-term stability and good reproducibility established this as a reliable approach for in vivo monitoring of NO in the hippocampus of rat brains following cerebral ischemia. This is the first report that the average level of NO increased from 61 ± 23 nM to 141 ± 18 nM after cerebral ischemia for 15 min.

Graphical abstract: A simple functional carbon nanotube fiber for in vivo monitoring of NO in a rat brain following cerebral ischemia

Supplementary files

Article information

Article type
Paper
Submitted
23 Jan 2017
Accepted
04 Mar 2017
First published
06 Mar 2017

Analyst, 2017,142, 1452-1458

A simple functional carbon nanotube fiber for in vivo monitoring of NO in a rat brain following cerebral ischemia

L. Liu, L. Zhang, Z. Dai and Y. Tian, Analyst, 2017, 142, 1452 DOI: 10.1039/C7AN00138J

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