School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 May 5;272:120997. doi: 10.1016/j.saa.2022.120997. Epub 2022 Feb 4.
Coronary heart disease (CHD) is one of the primary causes of death globally. There are several diagnostic techniques for CHD at present, but they are invasive and with limited accuracy. In the work, measurement of human urine based on surface-enhanced Raman spectroscopy (SERS) was proposed to diagnose CHD. Urine samples of 157 CHD patients and 63 healthy controls (HC) were investigated by SERS. Statistical analysis of the measured data was then performed. It was found that there were intensity differences in nine Raman peaks (1223/1243/1272/1463/1481/1516/1536/1541/1550 cm) between CHD and HC in their average SERS spectrum. Furthermore, principal component analysis (PCA)-linear discriminant analysis (LDA) was then utilized to establish a prediction model to classify CHD and HC. It revealed that the accuracy, specificity and sensitivity of the prediction model validated by leave-one-patient-out cross validation (LOPOCV) were 84.09%, 92.06% and 80.89%, respectively. Therefore, the proposed method can be employed as a non-invasive, rapid and accurate tool for CHD diagnosis in clinical application.
冠心病（CHD）是全球主要死因之一。目前有几种用于 CHD 的诊断技术，但它们具有侵入性且准确性有限。在这项工作中，提出了基于表面增强拉曼光谱（SERS）测量人尿液来诊断 CHD。对 157 例 CHD 患者和 63 例健康对照者（HC）的尿液样本进行了 SERS 研究。然后对测量数据进行了统计分析。结果发现，在其平均 SERS 光谱中，CHD 与 HC 之间在九个拉曼峰（1223/1243/1272/1463/1481/1516/1536/1541/1550 cm）处存在强度差异。此外，还利用主成分分析（PCA）-线性判别分析（LDA）建立了一个预测模型来对 CHD 和 HC 进行分类。通过留一患者交叉验证（LOPOCV）验证，该预测模型的准确率、特异性和灵敏度分别为 84.09%、92.06%和 80.89%。因此，该方法可作为一种非侵入性、快速、准确的临床 CHD 诊断工具。
