https://www.selleckchem.com/products/at13387.html ently associated with disease progression. Better ACE-27 scores appear to predict improved oncologic control.In this study, Surface Enhanced Raman Spectroscopy (SERS) was used for the characterization of Hepatitis C virus (HCV) in blood serum samples. For this purpose silver nanoparticles (Ag NPs) were used as substrates and SERS spectra were acquired from different clinically diagnosed HCV positive serum samples as well as from healthy individuals. Notably, same set of samples were also evaluated with Raman spectroscopy and SERS was found to be more helpful for the identification of the spectral features associated with the development of HCV infection. Different SERS features associated with the RNA bases were observed solely in the HCV positive serum as compared to the healthy samples which can be considered as SERS spectral markers of the HCV infection. Furthermore, principal component analysis (PCA) of the SERS spectral data was found to be very helpful in differentiation of spectral data of serum samples with different viral loads PLSR model was constructed to compare the capability of SERS and Raman analysis in the prediction of viral loads. It is found that SERS shows lower root mean square error of cross validation (RMSECV) and higher goodness of the model (R2) values than Raman data.The selectivity of single-amino acid nanosensors is still not well understood. Herein, the factors that govern graphene-based nanomaterials for the selective detection of lysine are reported to guide the design of single-amino acid nanosensors. Graphene quantum dots (GQDs), nitrogen-doped GQDs (N-GQDs), and nitrogen/sulfur co-doped GQDs (N,S-GQDs) were used to sense lysine. The interaction mode and mechanism adjusted selectivity of the zero-dimensional graphene-based quantum dots to lysine ascribe to the solution behavior, molecular size, number of atoms as electron donors in graphene, and driving force. Being a basic amino acid