Issue 12, 2018

The diagnosis of hepatic fibrosis by magnetic resonance and near-infrared imaging using dual-modality nanoparticles

Abstract

Hepatic fibrosis (HF), as the only reversible process of chronic liver disease, remains a big diagnostic challenge. Development of noninvasive and effective methods to assess quantitatively early-stage HF is of great clinical importance. Compared with conventional diagnostic methods, near-infrared fluorescence imaging (NIR) and magnetic resonance imaging (MRI) could offer highly sensitive and spatial resolution signals for HF detection. However, precise detection using contrast agents is not possible. Superparamagnetic iron oxide (SPIO) nanoparticles have low toxicity, high sensitivity and excellent biocompatibility. Integration of Fe3O4 nanoparticles and indocyanine green (ICG), coupled with targeting ligand of integrin αvβ3, arginine–glycine–aspartic acid (RGD) expressed on hepatic stellate cells (HSCs), were used to detect HF. Both in vivo and in vitro results showed that the SPIO@SiO2–ICG–RGD had high stability and low cytotoxicity. The biodistribution of SPIO@SiO2–ICG–RGD was significantly different between mice with HF and healthy controls. SPIO@SiO2–ICG–RGD was characterized and the results of imaging in vitro and in vivo demonstrated the expression of integrin αvβ3 on activated HSCs. These data suggest that our SPIO@SiO2–ICG–RGD probe could be used for the diagnosis of early-stage HF. This new nanoprobe with a dual-modality imaging approach holds great potential for the diagnosis and classification of HF.

Graphical abstract: The diagnosis of hepatic fibrosis by magnetic resonance and near-infrared imaging using dual-modality nanoparticles

Supplementary files

Article information

Article type
Paper
Submitted
01 Oct 2017
Accepted
24 Jan 2018
First published
12 Feb 2018
This article is Open Access
Creative Commons BY license

RSC Adv., 2018,8, 6699-6708

The diagnosis of hepatic fibrosis by magnetic resonance and near-infrared imaging using dual-modality nanoparticles

Y. Li, W. Shang, X. Liang, C. Zeng, M. Liu, S. Wang, H. Li and J. Tian, RSC Adv., 2018, 8, 6699 DOI: 10.1039/C7RA10847H

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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