Comparison of surface-enhanced Raman spectral data sets of filtrate portions of serum samples of hepatitis B and Hepatitis C infected patients obtained by centrifugal filtration.

BACKGROUND: 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. OBJECTIVES: 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). METHODOLOGY: 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. RESULTS: 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. CONCLUSIONS: 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.

Raman spectroscopic characterization of selenium N-heterocyclic carbene compounds.

In this study, Raman spectroscopy is employed to analyze and characterize two salts (N-heterocyclic carbene) and their respective selenium N-heterocyclic carbene compounds. The features observed as differences among Raman spectral data of two different N-heterocyclic carbene salts are called Salt-I and Salt-II and their respective Se compounds, called Compound-I & Compound-II, are used to confirm the formation covalent bond between Se atom carbon atom of carbene. Enhancement in peak intensities and shifting of peak positions is directly related with compound formation. Raman spectral data provide a detail information about bond formation, chemical and structural differences between salts and compounds. The observed Raman spectral features of both salts and compounds are in consistent with computationally calculated Raman spectral features. Raman spectral features of each salt and its respective compound was further analyzed with principal component analysis, which was found helpful for differentiating each salt from its respective compound.

Surface-enhanced Raman spectral analysis for comparison of PCR products of hepatitis B and hepatitis C.

BACKGROUND: Surface-enhanced Raman spectroscopy is a reliable tool for identification and differentiation of two diseases showing similar symptoms, hepatitis B (HBV) and hepatitis C (HCV). OBJECTIVES: To develop a polymerase chain reaction technique (PCR) based SERS technique for differentiation of two human pathological conditions sharing the same symptoms using multivariate data analysis techniques e.g. principle component analysis (PCA) and partial least square discriminate analysis (PLS-DA). METHODS: PCR products of HBV and HCV were differentiated by SERS using silver nanoparticles (AgNPs) as a SERS substrate. For this analysis, PCR products of both the diseases with predetermined viral loads were collected and analyzed under SERS instrument and unique SERS spectra of HBV and HCV was compared showing many differences at various points. Diseased classes of HBV and HCV and their negative control classes (viral load less than 1) were compared. PCR products of true healthy DNA and RNA were also compared, which were significantly separated. Moreover, SERS data was analyzed using multivariate data analysis techniques including principle component analysis (PCA) and partial least square discriminate analysis (PLS-DA) and differences were so prominent to observe. RESULTS: SERS spectral data of HBV and HCV showed clear differences and were significantly separated using PCA. Negative control samples of both disorders and their true healthy samples of DNA and RNA were separated according to 1st principle component. By analyzing data using partial least square discriminate analysis, differentiation of two disease classes was considered more valid with sensitivity, specificity and accuracy value of 96%, 94% and 98% respectively. Value of area under curve (AUROC) was 0.7527. CONCLUSION: SERS can be employed for identification and comparison of two human pathological conditions sharing the same symptomology.