Surface-enhanced Raman spectroscopy for characterization of filtrates of blood serum samples from patients with tuberculosis obtained by 50 kDa filtration devices.

The ability of surface-enhanced Raman spectroscopy (SERS) to generate spectroscopic fingerprints has made it an emerging tool for biomedical applications. The objective of this study is to confirm the potential use of Raman spectroscopy for early disease diagnosis based on blood serum. In this study, a total of sixty blood serum samples, consisting of forty from diseased patients and twenty (controls) from healthy individuals, was used. Because disease biomarkers, found in the lower molecular weight fraction, are suppressed by higher molecular weight proteins, 50 kDa Amicon ultrafiltration centrifugation devices were used to produce two fractions from whole blood serum consisting of a filtrate, which is a low molecular weight fraction, and a residue, which is a high molecular weight fraction. These fractions were then analyzed, and their SERS spectral data were compared with those of healthy fractions. The SERS technique was utilized on blood serum, filtrate and residue of patients with tuberculosis to identify characteristic SERS spectral features associated with the development of disease, which can be used to differentiate them from healthy samples using silver nanoparticles as a SERS substrate. For further analysis, the effective chemometric technique of principal component analysis (PCA) was used to qualitatively differentiate all the analyzed samples based on their SERS spectral features. Partial least squares discriminant analysis (PLS-DA) accurately classified the filtrate portions of healthy and tuberculosis samples with 97% accuracy, 97% specificity, 98% sensitivity, and an area under the receiver operating characteristic (AUROC) curve of 0.74.

Surface-enhanced Raman spectroscopy for comparison of biochemical profile of bacteriophage sensitive and resistant methicillin-resistant Staphylococcus aureus (MRSA) strains.

Methicillin-resistant Staphylococcus aureus (MRSA) is gram positive bacteria and leading cause of a wide variety of diseases. It is a common cause of hospitalized and community-acquired infections. Development of increasing antibiotic-resistance by methicillin-resistant S. aureus (MRSA) strains demand to develop alternate novel therapies. Bacteriophages are now widely used as antibacterial therapies against antibiotic-resistant gram-positive pathogens. So, there is an urgent need to find fast detection techniques to point out phage susceptible and resistant strains of methicillin-resistant S. aureus (MRSA) bacteria. Samples of two separate strains of bacteria, S. aureus, in form of pellets and supernatant, were used for this purpose. Strain-I was resistant to phage, while the other (strain-II) was sensitive. Surface Enhanced Raman Spectroscopy (SERS) has detected significant biochemical changes in these bacterial strains of pellets and supernatants in the form of SERS spectral features. The protein portion of these two types of strains of methicillin-resistant S. aureus (MRSA) in their relevant pellets and supernatants is major distinguishing biomolecule as shown by their representative SERS spectral features. In addition, multivariate data analysis techniques such as principal component analysis (PCA) and a partial least squares-discriminant analysis (PLS-DA) were found to be helpful in identifying and characterizing various strains of S. aureus which are sensitive and resistant to bacteriophage with 100% specificity, 100% accuracy, and 99.8% sensitivity in case of SERS spectral data sets of bacterial cell pellets. Moreover, in case of supernatant samples, the results of PLS-DA model including 95.5% specificity, 96% sensitivity, and 96.5% accuracy are obtained.

Characterization of structural changes occurring in insulin at different time intervals at room temperature by surface-enhanced Raman spectroscopy.

BACKGROUND: Insulin storage above the temperature recommended by food and drug administration (FDA) causes decrease in its functional efficacy due to degradation and aggregation of its protein based active pharmaceutical ingredient (API) that results poor glycemic control in diabetic patients. The aggregation of protein causes serious neurodegenerative diseases such as type-2 diabetes, Huntington disease, Parkinson's disease, and Alzheimer's disease. Surface-enhanced Raman spectroscopy (SERS) has been employed for the denaturation study of many proteins at the temperature above the recommendations of food and drug administration (FDA) (above 30 °C) which indicates potential of technique for such studies. OBJECTIVE: SERS along with multivariate discriminating analysis techniques-based analysis of degradation of liquid pharmaceutical insulin protein after regular intervals of time at room temperature to analyze the structural changes in this protein during the storage of insulin pharmaceutical at room temperature. METHODS: Silver nanoparticles (Ag-NPs) prepared by chemical reduction method are used as SERS active substrate for the surface enhancement of the insulin spectral signal. SERS spectral measurements of insulin were collected from eight different samples of insulin in the time range of 7 pm to 7 am first at fridge temperature (5 °C), second after half hour and next six with the time difference of 2 h each time at room temperature. The acquired SERS spectral data was preprocessed and analyzed. SERS structural transformations detection and discrimination potential in insulin was further confirmed by applying multivariate discriminating analysis techniques including principal component analysis (PCA) and Partial least square regression analysis (PLSR). RESULTS: SERS significantly detects the structural changes produced in insulin even after 2 h of insulin placement at room temperature. PCA successfully differentiates the insulin spectral data obtained after regular intervals of time according to PC-1 (77 %) explained variance. Application of PLSR model provides quantitative confirmation of SERS efficiency, by providing insulin data regression coefficients plot, efficient prediction of time with calibration data set having 0.77 mean square absolute error of calibration (RMSAEC), validation data set with 0.80 mean square absolute error of prediction (RMSAEP) and 0.98 coefficient of determination (R2) for both calibration and validation data set. CONCLUSION: SERS is proved as a highly sensitive and discriminating technique to detect and discriminate insulin structural changes after regular intervals of time at room temperature.

Chiral Recognition of Amino Acids Using CC2 Porous Organic Cages.

Enantiomers have the same physical properties but different chemical properties due to the difference in the orientation of groups in space and thus Chiral discrimination is quite necessary, as an enantiomer of drug can have lethal effects. In this study, we used the CC2 cage for chiral discrimination of amino acids using density functional theory. The results indicated the physisorption of amino acids in the central cavity of the cage. Among the four selected amino acids, proline showed maximum interactions with the cage and maximum chiral discrimination energy is also observed in the case of proline that is 2.78 kcal/mol. Quantum theory of atoms in molecules and noncovalent interaction index analyses showed that the S enantiomer in each case has maximum interactions. The charge transfer between the analyte and surface is further studied through natural bond orbital analysis. It showed sensitivity of cage for both enantiomers, but a more pronounced effect is seen for S enantiomers. In frontier molecular orbital analysis, the least EH-L gap is observed in the case of R proline with a maximum charge transfer of -0.24 e-. Electron density difference analysis is carried out to analyze the pattern of the charge distribution. The partial density of state analysis is computed to understand the contribution of each enantiomer in overall density of the complexes. Our results show that S-CC2 porous organic cages have a good ability to differentiate between two enantiomers. S-CC2 porous organic cages efficiently differentiated the S enantiomer from the R enantiomers of selected amino acids.

Density functional theory, molecular docking and in vivo muscle relaxant, sedative, and analgesic studies of indanone derivatives isolated from Heterophragma adenophyllum.

Heterophragma adenophyllum (HA) is an important medicinal plant which is used in traditional medicine for the treatment of muscular tension and pain. Herein, we report the isolation of methyl,1,2-dihydroxy-2-(3-methylbut-2-en-1-yl)-3-oxo-2,3-dihydro-1H-indene-1-carboxylate (1), from the roots of H. adenophyllum. The isolated compound 1 was evaluated for in vivo muscle relaxant, sedative, and analgesic potential in Swiss albino mice. Results revealed that the isolated compound 1 exhibited a dose- and time-dependent muscle coordination (51%) and a significant (p < 01) sedative effect. It also showed a considerable (p < 0.5) analgesia after 30 min of post treatment and was maintained for up-to 120 min of experimental duration. In acute toxicity studies, no mortality was observed which indicates a preliminary safety of compound 1. Furthermore, the experimental results were compared with the theoretical studies by using density functional theory (DFT). The stability of the compound as well as the flow of electrons was determined by the calculated Frontier orbital analysis. The calculated stretching frequencies, 1H-NMR/13C-NMR chemical shift values and UV-visible spectra were found to be in agreement with experimental values. The results obtained from molecular docking studies were used to explore the mechanism of analgesic and muscle relaxant activity.Communicated by Ramaswamy H. Sarma.