为肾癌诊断和分类设计一种集纳米酶活性和自组装于一体的双功能智能纳米平台

IF 16 1区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
ACS Nano Pub Date : 2024-08-27 Epub Date: 2024-08-16 DOI:10.1021/acsnano.4c08085
Dingyitai Liang, Shouzhi Yang, Ziqi Ding, Xiaoyu Xu, Wenxuan Tang, Yuning Wang, Kun Qian
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引用次数: 0

摘要

肾癌的准确诊断和分类对高质量的医疗服务至关重要。然而,目前的诊断平台在快速准确地分析大规模临床生物样本方面仍面临挑战。在此,我们基于单宁酸修饰金纳米流(TA@AuNFs)制备了一种双功能智能纳米平台,将纳米酶催化用于比色传感和自组装纳米阵列辅助 LDI-MS 分析。由于 AuNFs 表面有丰富的五倍子酰残基,TA@AuNFs 具有类似过氧化物酶(POD)和葡萄糖氧化酶的活性。结合比色法,基于 TA@AuNF 的传感纳米平台可直接检测血清中的葡萄糖,用于肾脏肿瘤的诊断。另一方面,TA@AuNFs可以通过Fe3+作为介质,在液-液界面自组装成紧密、均匀的二维(2D)纳米阵列。自组装的TA@AuNFs(SA-TA@AuNFs)阵列被用于辅助LDI-MS分析代谢物,表现出较高的电离效率和优异的质谱信号重现性。基于SA-TA@AuNF阵列辅助LDI-MS平台,我们成功地从尿液样本中提取了代谢指纹,实现了肾脏肿瘤的早期诊断、亚型分类以及良恶性肿瘤的鉴别。综上所述,我们开发的基于 TA@AuNF 的双功能智能纳米平台在临床疾病诊断、护理点检测和生物标记物发现方面显示出卓越的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Engineering a Bifunctional Smart Nanoplatform Integrating Nanozyme Activity and Self-Assembly for Kidney Cancer Diagnosis and Classification.

Engineering a Bifunctional Smart Nanoplatform Integrating Nanozyme Activity and Self-Assembly for Kidney Cancer Diagnosis and Classification.

Accurate diagnosis and classification of kidney cancer are crucial for high-quality healthcare services. However, the current diagnostic platforms remain challenges in the rapid and accurate analysis of large-scale clinical biosamples. Herein, we fabricated a bifunctional smart nanoplatform based on tannic acid-modified gold nanoflowers (TA@AuNFs), integrating nanozyme catalysis for colorimetric sensing and self-assembled nanoarray-assisted LDI-MS analysis. The TA@AuNFs presented peroxidase (POD)- and glucose oxidase-like activity owing to the abundant galloyl residues on the surface of AuNFs. Combined with the colorimetric assay, the TA@AuNF-based sensing nanoplatform was used to directly detect glucose in serum for kidney tumor diagnosis. On the other hand, TA@AuNFs could self-assemble into closely packed and homogeneous two-dimensional (2D) nanoarrays at liquid-liquid interfaces by using Fe3+ as a mediator. The self-assembled TA@AuNFs (SA-TA@AuNFs) arrays were applied to assist the LDI-MS analysis of metabolites, exhibiting high ionization efficiency and excellent MS signal reproducibility. Based on the SA-TA@AuNF array-assisted LDI-MS platform, we successfully extracted metabolic fingerprints from urine samples, achieving early-stage diagnosis of kidney tumor, subtype classification, and discrimination of benign from malignant tumors. Taken together, our developed TA@AuNF-based bifunctional smart nanoplatform showed distinguished potential in clinical disease diagnosis, point-of-care testing, and biomarker discovery.

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来源期刊
ACS Nano
ACS Nano 工程技术-材料科学:综合
CiteScore
26.00
自引率
4.10%
发文量
1627
审稿时长
1.7 months
期刊介绍: ACS Nano, published monthly, serves as an international forum for comprehensive articles on nanoscience and nanotechnology research at the intersections of chemistry, biology, materials science, physics, and engineering. The journal fosters communication among scientists in these communities, facilitating collaboration, new research opportunities, and advancements through discoveries. ACS Nano covers synthesis, assembly, characterization, theory, and simulation of nanostructures, nanobiotechnology, nanofabrication, methods and tools for nanoscience and nanotechnology, and self- and directed-assembly. Alongside original research articles, it offers thorough reviews, perspectives on cutting-edge research, and discussions envisioning the future of nanoscience and nanotechnology.
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