journal article·2010-05

Gd‐DTPA‐loaded PLGA microbubbles as both ultrasound contrast agent and MRI contrast agent—A feasibility research

Meng Ao, Zhigang Wang, Haitao Ran, Dajing Guo, Jinhong Yu, Ao Li, Weijuan Chen, Wei Wu, Yuanyi Zheng

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Abstract

Abstract Objective: To prepare the Gd‐DTPA‐loaded poly (lactic‐co‐glycolic acid) (PLGA) microbubbles for both ultrasound imaging and MRI imaging. Methods: Biodegradable high‐molecular‐weight poly (lactic‐co‐glycolic acid) (PLGA) was used as an encapsulating vehicle to fabricate a contrast agent composed of gadolinium diethylenetriamine pentaacetic acid (Gd‐DTPA) and fluorocarbon‐filled microbubbles by using double emulsion method. The morphology, size, and Gd loading capacity of PLGA microparticles were assessed. The efficacy of the contrast agent for ultrasound and magnetic resonance (MR) imaging was evaluated by in vivo imaging of hepatic vessels and the liver parenchyma of rabbit following injection of the contrast agent. Additionally, the liver and kidney functions were examined before and at 7, 14, 30 days after injection. Results: Electron microscopy showed Gd loaded PLGA microbubbles were spherical in shape. The mean diameter were 1.47 ± 0.38 μm and the Gd loading was 25 ± 2 μg/mg. Right after the injection of the PLGA microbubbles, the ultrasound echo intensity of hepatic vessels and the liver parenchyma was significantly enhanced. Moreover, enhanced signal intensity of the liver parenchyma on MR images was observed for ∼30 min after injection of the contrast agent. Neither marked changes in the liver and kidney functions nor other severe complications were detected during the follow‐up period. Conclusion: Gd‐DTPA‐loaded PLGA microbubbles could enhance both ultrasound imaging and MRI imaging. © 2010 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 2010.
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Research context

OpenAlex topics: Ultrasound and Hyperthermia Applications · Hepatocellular Carcinoma Treatment and Prognosis · Lanthanide and Transition Metal Complexes

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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