Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
Department of Chemistry, University of Agriculture Faisalabad, Faisalabad 38000, Pakistan.
Photodiagnosis Photodyn Ther. 2023 Jun;42:103532. doi: 10.1016/j.pdpdt.2023.103532. Epub 2023 Mar 22.
Surface-enhanced Raman spectroscopy (SERS) is an efficient technique which has been used for the analysis of filtrate portions of serum samples of Hepatitis B (HBV) and Hepatitis C (HCV) virus.
The main reason for this study is to differentiate and compare HBV and HCV serum samples for disease diagnosis through SERS. Hepatitis B and hepatitis C disease biomarkers are more predictable in their centrifuged form as compared in their uncentrifuged form. For differentiation of SERS spectral data sets of hepatitis B, hepatitis C and healthy person principal component analysis (PCA) proved to be a helpful. Centrifugally filtered serum samples of hepatitis B and hepatitis C are clearly differentiated from centrifugally filtered serum samples of healthy individuals by using partial least square discriminant analysis (PLS-DA).
Serum sample of HBV, HCV and healthy patients were centrifugally filtered to separate filtrate portion for studying biochemical changes in serum sample. The SERS of these samples is performed using silver nanoparticles as substrates to identify specific spectral features of both viral diseases which can be used for the diagnosis and differentiation of these diseases. The purpose of centrifugal filtration of the serum samples of HBV and HCV positive and control samples by using filter membranes of 50 KDa size is to eliminate the proteins bigger than 50 KDa so that their contribution in the SERS spectrum is removed and disease related smaller proteins may be observed. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are statistical tools which were used for the further validation of SERS.
HBV and HCV centrifugally filtered serum sample were compared and biomarkers including (uracil, phenylalanine, methionine, adenine, phosphodiester, proline, tyrosine, tryptophan, amino acid, thymine, fatty acid, nucleic acid, triglyceride, guanine and hydroxyproline) were identified through PCA and PLS-DA. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used as a multivariate data analysis tool for the diagnosis of the characteristic SERS spectral features associated with both types of viral diseases. For the classification and differentiation of centrifugally filtered HBV, HCV, and control serum samples, Principal component analysis is found helpful. Moreover, PLS-DA can classify these two distinct sets of SERS spectral data with 0.90 percent specificity, 0.85 percent precision, and 0.83 percent accuracy.
Surface enhanced Raman spectroscopy along with chemometric analysis like PCA and PLS-DA have been successfully differentiated HBV and HCV and healthy individuals' serum samples.
表面增强拉曼光谱（SERS）是一种有效的技术，已用于分析乙型肝炎（HBV）和丙型肝炎（HCV）病毒的血清样本滤液部分。
本研究的主要目的是通过 SERS 对 HBV 和 HCV 血清样本进行区分和比较，以进行疾病诊断。与未离心的形式相比，乙型肝炎和丙型肝炎疾病生物标志物在离心形式下更具可预测性。为了区分乙型肝炎、丙型肝炎和健康人 SERS 光谱数据集，主成分分析（PCA）被证明是有用的。通过偏最小二乘判别分析（PLS-DA），可以清楚地区分乙型肝炎和丙型肝炎离心过滤血清样本与健康个体的离心过滤血清样本。使用银纳米粒子作为基底，对 HBV、HCV 和健康患者的血清样本进行 SERS 分析，以鉴定这两种病毒疾病的特定光谱特征，可用于这些疾病的诊断和区分。使用 50 kDa 大小的滤膜对 HBV 和 HCV 阳性和对照样本的血清样本进行离心过滤的目的是消除大于 50 kDa 的蛋白质，从而去除它们在 SERS 光谱中的贡献，并观察与疾病相关的较小蛋白质。主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）是用于进一步验证 SERS 的统计工具。
比较了 HBV 和 HCV 离心过滤的血清样本，并通过 PCA 和 PLS-DA 鉴定了包括（尿嘧啶、苯丙氨酸、蛋氨酸、腺嘌呤、磷酸二酯、脯氨酸、酪氨酸、色氨酸、氨基酸、胸腺嘧啶、脂肪酸、核酸、甘油三酯、鸟嘌呤和羟脯氨酸）在内的生物标志物。主成分分析（PCA）和偏最小二乘判别分析（PLS-DA）被用作与两种病毒疾病相关的特征 SERS 光谱特征的诊断的多元数据分析工具。对于离心过滤的 HBV、HCV 和对照血清样本的分类和区分，主成分分析被证明是有帮助的。此外，PLS-DA 可以以 0.90%的特异性、0.85%的精度和 0.83%的准确率对这两组不同的 SERS 光谱数据进行分类。
表面增强拉曼光谱与 PCA 和 PLS-DA 等化学计量分析相结合，成功地区分了 HBV 和 HCV 以及健康个体的血清样本。
