State Key Laboratory of Physical Chemistry of Solid Surfaces, The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, and The Key Laboratory for Chemical Biology of Fujian Province, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
Department of Cerebrovascular Diseases, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan, China.
ACS Appl Mater Interfaces. 2022 Jan 26;14(3):3784-3791. doi: 10.1021/acsami.1c20617. Epub 2022 Jan 12.
Manganese oxide nanoparticles (NPs) have attracted increasing attention recently as contrast agents (CAs) for magnetic resonance imaging (MRI). However, the clinical translation and popularization of conventional MnO NPs are hampered by their relatively poor imaging performance. Herein, we report the construction of ultrasmall MnO NPs (USMnO) a one-pot synthetic approach that show a much better capability of -weighted contrast enhancement for MRI ( = 15.6 ± 0.4 mM s at 0.5 T) than MnCl and conventional large-sized MnO NPs (MnO-22). These USMnO are further coated with zwitterionic dopamine sulfonate (ZDS) molecules, which improves their biocompatibility and prevents nonspecific binding of serum albumins. Interestingly, USMnO@ZDS are capable of passing through the blood-brain barrier (BBB), which enables the acquisition of clear images showing brain anatomic structures with -weighted contrast-enhanced MRI. Therefore, our USMnO@ZDS could be used as a promising MRI CA for the flexible and accurate diagnosis of brain diseases, which is also instructive for the construction of manganese-based CA with a high MRI performance.
近年来，氧化锰纳米粒子（NPs）作为磁共振成像（MRI）的对比剂（CAs）引起了越来越多的关注。然而，由于其成像性能相对较差，传统 MnO NPs 的临床转化和推广受到了阻碍。在此，我们报告了超小 MnO NPs（USMnO）的构建-一种一锅合成方法，其用于 MRI 的 T1 加权对比增强能力明显优于 MnCl2 和传统的大尺寸 MnO NPs（MnO-22）（=15.6±0.4mM s，在 0.5 T 下）。这些 USMnO 进一步被带负电荷的多巴胺磺酸盐（ZDS）分子包覆，提高了其生物相容性并防止了血清白蛋白的非特异性结合。有趣的是，USMnO@ZDS 能够穿透血脑屏障（BBB），从而可以获得清晰的图像，显示 T1 加权对比增强 MRI 的脑解剖结构。因此，我们的 USMnO@ZDS 可用作一种有前途的 MRI CA，用于灵活、准确地诊断脑部疾病，这也为构建具有高 MRI 性能的基于锰的 CA 提供了启示。
