Anti-stokes luminescent organic nanoparticles for frequency upconversion biomedical imaging

IF 4.7 4区 医学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Guobo Chen M.S. , Yuhao Li PhD , Jinliang Liu M.S. , Gang Huang MD, PhD , Qiwei Tian PhD
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引用次数: 2

Abstract

Frequency upconversion optical imaging has attracted great attention due to its remarkable advantages over traditional down-conversion optical imaging. However, the development of frequency upconversion optical imaging is extremely limited. Herein, five derivatives with BODIPY structure (B1–B5) were developed to investigate its frequency upconversion luminescence (FUCL) performance by introducing electron-donating and electron-withdrawing groups. Except for the nitro group decorated derivative, the other derivatives have strong and stable FUCL around 520 nm under 635 nm light excitation. More importantly, B5 retains FUCL ability after self-assembly. When applied to FUCL imaging of cells, B5 nanoparticles can be enriched in the cytoplasm and show a good signal-to-noise ratio. Meanwhile, FUCL tumor imaging can be achieved after 1 h of injection. This study not only provides a potential agent for FUCL biomedical imaging but also develops a new strategy for designing FUCL agents that exhibit excellent performance.

Abstract Image

用于上变频生物医学成像的抗斯托克斯发光有机纳米颗粒
频率上变频光学成像以其相对于传统下变频光学成像的显著优势而备受关注。然而,上变频光学成像技术的发展非常有限。本文通过引入供电子和吸电子基团,制备了5种具有BODIPY结构的衍生物(B1-B5),研究了其频率上转换发光(FUCL)性能。除硝基修饰衍生物外,其他衍生物在635 nm光激发下,在520 nm附近具有强而稳定的FUCL。更重要的是,B5自组装后保留了FUCL能力。应用于细胞FUCL成像时,B5纳米颗粒在细胞质中富集,表现出良好的信噪比。同时,注射1 h后可实现FUCL肿瘤成像。本研究不仅为FUCL生物医学成像提供了一种潜在的试剂,而且为设计性能优异的FUCL试剂开辟了新的策略。
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来源期刊
CiteScore
8.10
自引率
3.60%
发文量
104
审稿时长
4.6 months
期刊介绍: Nanomedicine: Nanotechnology, Biology and Medicine (NBM) is an international, peer-reviewed journal presenting novel, significant, and interdisciplinary theoretical and experimental results related to nanoscience and nanotechnology in the life and health sciences. Content includes basic, translational, and clinical research addressing diagnosis, treatment, monitoring, prediction, and prevention of diseases.
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