RESUMEN
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.
Asunto(s)
Compuestos Heterocíclicos , Compuestos de Selenio , Selenio , Metano/análogos & derivados , Espectrometría RamanRESUMEN
Hepatitis B is a contagious liver disorder caused by hepatitis B virus and if not treated at an early stage, it becomes chronic and results in liver cirrhosis and hepatocellular carcinoma which can even lead to death. In present study, surface-enhanced Raman spectroscopy (SERS) is employed for the analysis of polymerase chain reaction (PCR) products of DNA extracted from hepatitis B virus (HBV) infected patients in comparison with healthy individuals. SERS spectral features are identified which are solely present in the HBV positive samples and consistently increase in intensities with increase in viral load which can be considered as a SERS spectral marker for HBV infection. For sake of understanding, these various levels of viral loads in this study are classified as low (1-1000 IU), medium (1000-10,000 IU), high (above 10,000 IU) and negative control (>1). In order to explore the efficiency of SERS for discrimination of SERS spectral datasets of different samples of varying viral loads and healthy individuals, principal component analysis (PCA) is applied. PCA is used for comparison of these classes including low, medium and high levels of viral loads with each other and with healthy class. Moreover, partial least square discriminant analysis and partial least square regression analysis are employed for the classification of different levels of viral loads in the HBV positive samples and prediction of viral loads in the unknown samples, respectively. PLS-DA is applied for validity of classification and its sensitivity and specificity was found to be 89% and 98% respectively. PLSR model was constructed for prediction of viral loads on the bases of SERS spectral markers of HBV infection with goodness value of 0.9031 and value of root means square error (RMSE) 0.2923. PLSR model also proved to be valid for prediction of blind sample.
Asunto(s)
Hepatitis B Crónica , Hepatitis B , Neoplasias Hepáticas , ADN Viral/genética , Hepatitis B/diagnóstico , Virus de la Hepatitis B/genética , Humanos , Reacción en Cadena de la Polimerasa , Carga ViralRESUMEN
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 HCV from blood samples. METHODS: SERS was employed to characterize the Hepatitis C viral RNA extracted from different blood samples of hepatitis C virus (HCV) infected patients with predetermined viral loads in comparison with total RNA of healthy individuals. The SERS measurements were performed on 27 extracted RNA samples including low viral loads, medium viral loads, high viral loads and healthy/negative viral load samples. For this purpose, silver nanoparticles (Ag NPs) were used as SERS substrates. Furthermore, multivariate data analysis technique, 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 the extracted RNA samples associated with the increasing viral loads were established which could be employed for HCV diagnostic purpose. PCA was found helpful for the differentiation between Raman spectral data of RNA extracted from hepatitis infected and healthy blood samples. PLSR model is established for the determination of viral loads in HCV positive RNA samples with 99 % accuracy. CONCLUSION: SERS can be employed for qualitative and quantitative analysis of HCV from blood samples.