Upconversion and NIR-II luminescent rare earth nanoparticles combined with machine learning for cancer theranostics†

IF 5.1 3区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Nanoscale Pub Date : 2024-08-08 DOI:10.1039/D4NR01861C
Hanyu Liu, Ziyue Ju, Xin Hui, Wenjing Li and Ruichan Lv
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引用次数: 0

Abstract

How to develop contrast agents for cancer theranostics is a meaningful and challenging endeavor, and rare earth nanoparticles (RENPs) may provide a possible solution. In this study, we initially modified RENPs through the application of photodynamic agents (ZnPc) and targeted the bevacizumab antibody for cancer theranostics, which was aimed at improving the therapeutic targeting and efficacy. Subsequently, we amalgamated anthocyanin with the modified RENPs, creating a potential cancer diagnosis platform. When the spectral data were obtained from the composite of cells, the crucial information was extracted through a competitive adaptive reweighted sampling feature algorithm. Then, we employed a machine learning classification model and classified both the individual spectral data and fused spectral data to accurately predict distinctions between breast cancer and normal tissue. The results indicate that the amalgamation of fusion techniques with machine learning algorithms provides highly precise predictions for molecular-level breast cancer detection. Finally, in vitro and in vivo experiments were carried out to validate the near-infrared luminescence and therapeutic effectiveness of the modified nanomedicine. This research not only underscores the targeted effects of nanomedicine but also demonstrates the potent synergy between optical spectral technology and machine learning. This innovative approach offers a comprehensive strategy for the integrated treatment of breast cancer.

Abstract Image

上转换和近红外 II 发光稀土纳米粒子与机器学习相结合,用于癌症疗法
如何开发用于癌症治疗的造影剂是一项有意义且极具挑战性的工作,而稀土纳米粒子(RENPs)可能是一种可行的解决方案。在本研究中,我们首先通过应用光动力药剂(ZnPc)和靶向贝伐珠单抗(Bevacizumab)对 RENPs 进行了改性,以提高其治疗靶向性和疗效。随后,我们将花青素与经修饰的 RENPs 相结合,创建了一个潜在的癌症诊断平台。当从细胞复合体中获得光谱数据时,通过竞争性自适应重加权采样特征算法提取关键信息。然后,我们采用机器学习分类模型,对单个光谱数据和融合光谱数据进行分类,以准确预测乳腺癌和正常组织之间的区别。结果表明,融合技术与机器学习算法的结合为分子水平的乳腺癌检测提供了高度精确的预测。最后,通过体外和体内实验验证了改良纳米药物的近红外发光和治疗效果。这项研究不仅强调了纳米药物的靶向效应,还展示了光学光谱技术与机器学习之间的强大协同作用。这种创新方法为乳腺癌的综合治疗提供了一种全面的策略。
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来源期刊
Nanoscale
Nanoscale CHEMISTRY, MULTIDISCIPLINARY-NANOSCIENCE & NANOTECHNOLOGY
CiteScore
12.10
自引率
3.00%
发文量
1628
审稿时长
1.6 months
期刊介绍: Nanoscale is a high-impact international journal, publishing high-quality research across nanoscience and nanotechnology. Nanoscale publishes a full mix of research articles on experimental and theoretical work, including reviews, communications, and full papers.Highly interdisciplinary, this journal appeals to scientists, researchers and professionals interested in nanoscience and nanotechnology, quantum materials and quantum technology, including the areas of physics, chemistry, biology, medicine, materials, energy/environment, information technology, detection science, healthcare and drug discovery, and electronics.
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