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高波数拉曼光谱在正常受试者、口腔癌前病变和恶性患者尿液代谢物特征分析中的应用。

High wavenumber Raman spectroscopy in the characterization of urinary metabolites of normal subjects, oral premalignant and malignant patients.

机构信息

Department of Medical Physics, Anna University, Chennai, India.

Department of Medical Physics, Anna University, Chennai, India.

出版信息

Spectrochim Acta A Mol Biomol Spectrosc. 2017 Jan 15;171:52-59. doi: 10.1016/j.saa.2016.06.048. Epub 2016 Jul 16.

Abstract

Urine has emerged as one of the diagnostically potential bio fluids, as it has many metabolites. As the concentration and the physiochemical properties of the urinary metabolites may vary under pathological transformation, Raman spectroscopic characterization of urine has been exploited as a significant tool in identifying several diseased conditions, including cancers. In the present study, an attempt was made to study the high wavenumber (HWVN) Raman spectroscopic characterization of urine samples of normal subjects, oral premalignant and malignant patients. It is concluded that the urinary metabolites flavoproteins, tryptophan and phenylalanine are responsible for the observed spectral variations between the normal and abnormal groups. Principal component analysis-based linear discriminant analysis was carried out to verify the diagnostic potentiality of the present technique. The discriminant analysis performed across normal and oral premalignant subjects classifies 95.6% of the original and 94.9% of the cross-validated grouped cases correctly. In the second analysis performed across normal and oral malignant groups, the accuracy of the original and cross-validated grouped cases was 96.4% and 92.1% respectively. Similarly, the third analysis performed across three groups, normal, oral premalignant and malignant groups, classifies 93.3% and 91.2% of the original and cross-validated grouped cases correctly.

摘要

尿液已经成为一种具有诊断潜力的生物流体,因为它有许多代谢物。由于尿液代谢物的浓度和物理化学性质可能在病理转化下发生变化,因此尿液的拉曼光谱特征已被用作识别多种疾病状态(包括癌症)的重要工具。在本研究中,尝试研究了正常受试者、口腔癌前病变和恶性患者的尿液样本的高波数(HWVN)拉曼光谱特征。结论是,尿液代谢物黄素蛋白、色氨酸和苯丙氨酸负责观察到的正常组和异常组之间的光谱变化。基于主成分分析的线性判别分析用于验证本技术的诊断潜力。对正常和口腔癌前病变组进行的判别分析正确分类了 95.6%的原始和 94.9%的交叉验证分组病例。在对正常和口腔恶性组进行的第二次分析中,原始和交叉验证分组病例的准确性分别为 96.4%和 92.1%。同样,对正常、口腔癌前病变和恶性三组进行的第三次分析正确分类了 93.3%和 91.2%的原始和交叉验证分组病例。

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