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利用拉曼光谱检测疾病前期的实践、潜力与展望

Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy.

作者信息

Oshima Yusuke, Haruki Takayuki, Koizumi Keiichi, Yonezawa Shota, Taketani Akinori, Kadowaki Makoto, Saito Shigeru

机构信息

Faculty of Engineering, University of Toyama, Toyama 930-8555, Japan.

Research Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, Japan.

出版信息

Int J Mol Sci. 2023 Jul 29;24(15):12170. doi: 10.3390/ijms241512170.

DOI:10.3390/ijms241512170
PMID:37569541
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10418989/
Abstract

Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a "molecular fingerprint" for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.

摘要

拉曼光谱在实际临床应用中显示出巨大潜力。通过对来自活细胞和组织的散射光进行实时、无损测量来分析分子的结构和组成,它提供了有价值的见解。拉曼光谱数据直接与细胞和组织的分子组成相关联,并为各种疾病状态提供“分子指纹”。本综述重点关注拉曼光谱的实际和临床应用,尤其是在人类疾病的早期检测方面。识别疾病前期(即标志着从健康状态向疾病状态转变的阶段)对于预防疾病发生的有效干预措施至关重要。拉曼光谱可以揭示过渡状态期间发生的生物过程,并最终可能检测出疾病前期状态下的分子动态。

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Raman spectroscopy and machine learning unveil biomolecular alterations in invasive breast cancer.拉曼光谱和机器学习揭示浸润性乳腺癌中的生物分子改变。
J Biomed Opt. 2023 Mar;28(3):036009. doi: 10.1117/1.JBO.28.3.036009. Epub 2023 Mar 30.
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Raman Spectroscopy for Early Detection of Cervical Cancer, a Global Women's Health Issue-A Review.拉曼光谱在宫颈癌早期检测中的应用——全球女性健康问题综述
利用动态网络生物标志物理论评估与年龄相关的病理发展的新可能性。
Cells. 2023 Sep 17;12(18):2297. doi: 10.3390/cells12182297.
Molecules. 2023 Mar 9;28(6):2502. doi: 10.3390/molecules28062502.
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H-CNN combined with tissue Raman spectroscopy for cervical cancer detection.H-CNN 结合组织拉曼光谱用于宫颈癌检测。
Spectrochim Acta A Mol Biomol Spectrosc. 2023 Apr 15;291:122339. doi: 10.1016/j.saa.2023.122339. Epub 2023 Jan 12.
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Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning.基于拉曼光谱和深度学习的肝癌快速无标记组织病理学诊断。
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