Surface-enhanced Raman spectroscopy for comparison of serum samples of typhoid and tuberculosis patients of different stages.

BACKGROUND: Surface-enhanced Raman spectroscopy (SERS) is a reliable tool for the identification and differentiation of two different human pathological conditions sharing the same symptomology, typhoid and tuberculosis (TB). OBJECTIVES: To explore the potential of surface-enhanced Raman spectroscopy for differentiation of two different diseases showing the same symptoms and analysis by principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA). METHODS: Serum samples of clinically diagnosed typhoid and tuberculosis infected individuals were analyzed and differentiated by SERS using silver nanoparticles (Ag NPs) as a SERS substrate. For this purpose, the collected serum samples were analyzed under the SERS instrument and unique SERS spectra of typhoid and tuberculosis were compared showing notable spectral differences in protein, lipid and carbohydrates features. Different stages of the diseased class of typhoid (Early acute and late acute stage) and tuberculosis (Pulmonary and extra-pulmonary stage) were compared with each other and with healthy human serum samples, which were significantly separated. Moreover, SERS data was analyzed using multivariate data analysis techniques including principal component analysis (PCA) and partial least square discriminate analysis (PLS-DA) and differences were so prominent to observe. RESULTS: SERS Spectral data of typhoid and tuberculosis showed clear differences and were significantly separated using PCA. SERS spectral data of both stages of typhoid and tuberculosis were separated according to 1st principle component. Moreover, by analyzing data using partial least square discriminate analysis, differentiation of two disease classes were considered more valid with a 100% value of sensitivity, specificity and accuracy. CONCLUSION: SERS can be employed for identification and comparison of two different human pathological conditions sharing same symptomology.

Characterization and prediction of viral loads of Hepatitis B serum samples by using surface-enhanced Raman spectroscopy (SERS).

BACKGROUND: Raman spectroscopy is a promising technique to analyze the body fluids for the purpose of non-invasive disease diagnosis. OBJECTIVES: To develop a surface-enhanced Raman spectroscopy (SERS) based method for qualitative and quantitative analysis of hepatitis B viral (HBV) infection from blood serum samples. METHODS: Clinically diagnosed hepatitis B virus (HBV) infected serum samples of patients of different levels of viral loads have been subjected for SERS analysis in comparison with the healthy ones by using silver nanoparticles (Ag NPs) based SERS substrates. The SERS measurements were performed on blood serum samples of 11 healthy and 32 clinically diagnosed HBV patients of different viral load levels of different exponentials including (101, 102 called as low level), (103, 104 called as medium level) and (105, 108 called as high level). Furthermore, multivariate data analysis techniques, Principal Component Analysis (PCA) and Partial Least Square Regression (PLSR) were also performed on SERS spectral data. RESULTS: The SERS spectral features due to biochemical changes in HBV positive serum samples associated with the increasing viral loads were established which could be employed for HBV diagnostic purpose. PCA was found helpful for the differentiation between SERS spectral data of serum samples of different levels of HBV infection and healthy individuals. PLSR model developed with standard samples of known viral loads for predicting the viral loads of blind/unknown samples with 99% predicted accuracy. CONCLUSION: SERS can be employed for qualitative and quantitative analysis of HBV infection from blood serum samples.

Surface-enhanced Raman spectroscopy analysis of serum samples of typhoid patients of different stages.

BACKGROUND: Surface-enhanced Raman spectroscopy (SERS) of body fluids is considered a quick, simple and easy to use method for the diagnosis of disease. OBJECTIVES: To evaluate rapid, reliable, and non-destructive SERS-based diagnostic tool with multivariate data analysis including principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) for classification of different stages of typhoid on the basis of characteristic SERS spectral features. METHODS: SERS has been used for analysis of serum samples of different stages of typhoid including early acute stage and late acute stage in comparison with healthy samples, in order to investigate capability of this technique for diagnosis of typhoid. SERS spectral features associated with the biochemical changes taking place during the development of the typhoid fever were analyzed and identified. RESULTS: The value of area under the receiver operating characteristics (AUROC) for early acute stage versus healthy is 0.87 and that for healthy versus late acute stage is 0.52. PLS-DA classifier model gives values of 100 % for accuracy, sensitivity and specificity, respectively for the SERS spectral data sets of healthy versus early acute stage. Moreover, this classifier model gives values of 91 %, 89 % and 97 % for accuracy, sensitivity and specificity, respectively for the SERS spectral data sets of healthy versus late acute stage. CONCLUSIONS: Based on preliminary work it is concluded that SERS has potential to diagnose various stages of typhoid fever including early acute and late acute stage in comparison with healthy samples.