Department of Chemistry, University of Agriculture, Faisalabad 38040, Pakistan.
Department of Chemistry, University of Agriculture, Faisalabad 38040, Pakistan.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Oct 5;259:119908. doi: 10.1016/j.saa.2021.119908. Epub 2021 May 5.
In the current study, for a qualitative and quantitative study of Polymerase Chain Reaction (PCR) products of viral RNA of Hepatitis C virus (HCV) infection, surface-enhanced Raman spectroscopy (SERS) methodology has been developed. SERS was used to identify the spectral features associated with the PCR products of viral RNA of Hepatitis C in various samples of HCV-infected patients with predetermined viral loads. The measurements for SERS were performed on 30 samples of PCR products, which included three PCR products of RNA of healthy individuals, six negative controls, and twenty-one HCV positive samples of varying viral loads (VLs) using Silver nanoparticles (Ag NPs) as a SERS substrates. Additionally, on SERS spectral data, the multivariate data analysis methods including Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) were also carried out which help to illustrate the diagnostic capabilities of this method. The PLSR model is designed to predict HCV viral loads based on biochemical changes observed as SERS spectral features which can be associated directly with HCV RNA. Several SERS characteristic features are observed in the RNA of HCV which are not detected in the spectra of healthy RNA/controls. PCA is found helpful to differentiate the SERS spectral data sets of HCV RNA samples from healthy and negative controls. The PLSR model is found to be 99% accurate in predicting VLs of HCV RNA samples of unknown samples based on SERS spectral changes associated with the Hepatitis C development.
在本研究中，为了对丙型肝炎病毒（HCV）感染的病毒 RNA 的聚合酶链反应（PCR）产物进行定性和定量研究，开发了表面增强拉曼光谱（SERS）方法。SERS 用于鉴定与 HCV 感染患者各种样本中 HCV 病毒 RNA 的 PCR 产物相关的光谱特征，这些样本的病毒载量（VL）预先确定。对使用银纳米粒子（Ag NPs）作为 SERS 基底的 30 个 PCR 产物样本进行了 SERS 测量，其中包括三个健康个体的 RNA 的 PCR 产物、六个阴性对照和 21 个具有不同 VL 的 HCV 阳性样本。此外，还对 SERS 光谱数据进行了多元数据分析方法，包括主成分分析（PCA）和偏最小二乘回归（PLSR），这些方法有助于说明该方法的诊断能力。PLSR 模型旨在根据观察到的生化变化预测 HCV VL，这些变化可以直接与 HCV RNA 相关联。在 HCV RNA 中观察到几个 SERS 特征，而在健康 RNA/对照的光谱中未检测到这些特征。PCA 有助于区分 HCV RNA 样本与健康和阴性对照的 SERS 光谱数据集。根据与丙型肝炎发展相关的 SERS 光谱变化，PLSR 模型发现可以 99%准确地预测未知样本中 HCV RNA 的 VL。
