Surface enhanced Raman spectroscopy for the characterization of the metabolites of the biodesulfurization of dibenzothiophene carried out by Tsukamurella paurometabola.

Large amount of sulphur is released by the combustion of fossil fuels in the form of SoX which affects human health and leads to acid rain. To overcome this issue, it is essential to eliminate sulphur moieties from heterocyclic organo-sulphur compounds like Dibenzothiophene (DBT) present in the petrol. In this study Surface enhanced Raman scattering (SERS) spectroscopy is used to analyze the desulfurizing activity of Tsukamurella paurometabola bacterial strain. The most prominent SERS peaks observed at 791, 837, 944 and 1032 cm-1, associated to C-S stretching, are solely observed in dibenzothiophene and its metabolite-I (DBTS) but absent in 2-Hydroxybiphenyl (metabolite-II) and extraction sample of supernatant as a result of biodesulfurization. Moreover, the SERS peaks observed at 974 (characteristic peak of benzene ring) and 1015 cm-1 is associated to C-C ring breathing while 1642 and 1655 cm-1 assigned to CC bonds of aromatic ring. These peaks are only observed in 2-Hydroxybiphenyl (metabolite-II) and extraction sample of supernatant as a result of biodesulfurization. Notably, these peaks are absent in the Dibenzothiophene and its metabolite-I which indicate that aromatic ring is carrying sulfur in this fraction. Moreover, multivariate data analytical tools like principal component analysis (PCA) and PCA-loadings are applied to further differentiate between dibenzothiophene and its metabolites that are Dibenzothiophene sulphone (metabolite-I) and 2-Hydroxybiphenyl (metabolite-II).

Rapid Identification and Quantification of Adulteration in Methyl Eugenol using Raman Spectroscopy Coupled with Multivariate Data Analysis.

Identification of adulterants in commercial samples of methyl eugenol is necessary because it is a botanical insecticide, a tephritid male attractant lure that is used to attract and kill invasive pests such as oriental fruit flies and melon flies on crops. In this study, Raman spectroscopy was used to qualitatively and quantitatively assess commercial methyl eugenol along with adulterants. For this purpose, commercial methyl eugenol was adulterated with different concentrations of xylene. The Raman spectral features of methyl eugenol and xylene in liquid formulations were examined, and Raman peaks were identified as associated with the methyl eugenol and adulterant. Principal component analysis (PCA) and partial least-squares regression analysis (PLSR) have been used to qualitatively and quantitatively analyze the Raman spectral features. PCA was applied to differentiate Raman spectral data for various concentrations of methyl eugenol and xylene. Additionally, PLSR has been used to develop a predictive model to observe a quantitative relationship between various concentrations of adulterated methyl eugenol and their Raman spectral data sets. The root-mean-square errors of calibration and prediction were calculated using this model, and the results were found to be 1.90 and 3.86, respectively. The goodness of fit of the PLSR model is found to be 0.99. The proposed approach showed excellent potential for the rapid, quantitative detection of adulterants in methyl eugenol, and it may be applied to the analysis of a range of pesticide products.

Surface-Enhanced Raman Scattering (SERS) in Combination with PCA and PLS-DA for the Evaluation of Antibacterial Activity of 1-Isopentyl-3-pentyl-1H-imidazole-3-ium Bromide against Bacillus subtilis.

In the current study, surface-enhanced Raman scattering (SERS) was performed to evaluate the antibacterial activity of lab-synthesized drug (1-isopentyl-3-pentyl-1H-imidazole-3-ium bromide salt) and commercial drug tinidazole againstBacillus subtilis. The changes in SERS spectral features were studied for unexposed bacillus and exposed one with various dosages of drug synthesized in the lab (1-isopentyl-3-pentyl-1H-imidazole-3-ium bromide salt), and SERS bands were assigned associated with the drug-induced biochemical alterations in bacteria. Multivariate data analysis tools including principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) have been utilized to analyze the antibacterial activity of the imidazole derivative (lab drug). PCA was employed in differentiating all the SERS spectral data sets associated with the various doses of the lab-synthesized drug. There is clear discrimination among the spectral data sets of a bacterial strain treated with different concentrations of the drug, which are analyzed by PLS-DA with 86% area under the curve in receiver operating curve (ROC), 99% sensitivity, 100% accuracy, and 98% specificity. Various dominant spectral features are observed with a gradual increase in the different concentrations of the applied drug including 715, 850, 1002, 1132, 1237, 1396, 1416, and 1453 cm-1, which indicate the possible biochemical changes caused in bacteria during the antibacterial activity of the lab-synthesized drug. Overall, the findings show that imidazole and imidazolium compounds generated from tinidazole with various alkyl lengths in the amide substitution can be effective antibacterial agents with low cytotoxicity in humans, and these results indicate the efficiency of SERS in pharmaceuticals and biomedical applications.

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.

Quantitative analysis of cephalexin in solid dosage form by Raman spectroscopy and chemometric tools.

OBJECTIVE: To use Raman Spectroscopy for qualitative and quantitative evaluation of pharmaceutical formulations of active pharmaceutical ingredient (API) of Cephalexin. SIGNIFICANCE: Raman Spectroscopy is a noninvasive, nondestructive, reliable and rapid detection technique used for various pharmaceutical drugs quantification. The present study explores the potential of Raman Spectroscopy for quantitative analysis of pharmaceutical drugs. METHOD: For qualitative and quantitative analysis of Cephalexin API, various standard samples containing less and more concentration of API than commercial tablet was prepared. To study spectral differences, the mean plot of all the samples was prepared. For qualitative analysis, Principal Component Analysis (PCA) and for quantitative analysis Partial Least Square Regression analysis (PLSR) was used. Both of these are Multivariate data analysis techniques and give reliable results as published in previous literature. RESULTS: PCA model distinguished all the Raman Spectral data related to the various Cephalexin solid dosage formulations whereas the PLSR model was used to calculate the concentration of different unknown formulations. For the PLSR model, RMSEC and RMSEP were determined to be 3.3953 and 3.8972, respectively. The prediction efficiency of this built PLSR model was found to be very good with a goodness of the model value (R2) of 0.98. The PLSR model also predicted the concentrations of Cephalexin formulations in the blind or unknown sample. CONCLUSION: These findings demonstrate that the Raman spectroscopy coupled to PLSR analysis could be regarded as a fast and effectively reliable tool for quantitative analysis of pharmaceutical drugs.

Characterization of three different benzimidazolium ligands and their organo-selenium complexes by using density functional theory and Raman spectroscopy.

In the present study, Raman spectroscopy (RS) along with density functional theory (DFT) calculations have been performed for the successful characterization and confirmation of the formation of three different selenium-based N-heterocyclic carbene (NHC) complexes from their respective salts. For this purpose, mean RS features and DFT calculations of different ligands and their respective selenium NHC complexes are compared. The identified characteristic RS and DFT features, of each of these ligands and their selenium complexes, show that the polarizability of benzimidazolium rings increases after complex formation with selenium. This has been shown by the enhanced intensity of the associated Raman peaks, therefore, confirming the formation of newly formed bonds. The complex formation is also confirmed by the identification of several new peaks in the spectra of complexes and these Raman bands were absent in the spectra of the ligands. Moreover, Raman spectral data sets are analyzed using a multivariate data analysis technique of Principal Component Analysis (PCA) to observe the efficiency of the RS analysis. The results presented in this study have proved the RS technique, along with DFT, an undoubtedly fast approach for the confirmation of synthesis of selenium based NHC-complexes.

Raman Spectroscopy for the Quantitative Analysis of Solid Dosage Forms of the Active Pharmaceutical Ingredient of Febuxostat.

Raman spectroscopy has been used to characterize and quantify the solid dosage forms of the commercially available drug febuxostat. For this purpose, different formulations consisting of the febuxostat (API) and excipients with different concentrations of the API are prepared and analyzed by Raman spectroscopy to identify different spectral features related to the febuxostat API and excipients. Multivariate data analysis tools such as principal component analysis (PCA) and partial least-squares regression (PLSR) analysis are used for qualitative and quantitative analyses. PCA has been found to be useful for the qualitative monitoring of various solid dosage forms. PLSR analysis has led to the successful prediction of API concentration in the unknown samples with a sensitivity and a selectivity of 98 and 99%, respectively. Moreover, the root-mean-square error (RMSE) of calibration and validation of the PLSR model has been found to be 2.9033 and 1.35, respectively. Notably, it is found to be very helpful for the comparison between the self-made formulations of febuxostat and commercially available febuxostat tablets (40 and 80 mg) of two different brands (Gouric and Zurig). These results showed that Raman spectroscopy can be a useful and reliable technique for identifying and quantifying the active pharmaceutical ingredient (API) in commercially available solid dosage forms.

Synthesis, Characterization, and Evaluation of Antifungal Activity of 1-Butyl-3-hexyl-1H-imidazol-2(3H)-selenone by Surface-Enhanced Raman Spectroscopy.

In the present research work, a selenium N-heterocyclic carbene (Se-NHC) complex/adduct was synthesized and characterized by using different analytical methods including FT-IR, 1HNMR, and 13CNMR. The antifungal activity of the Se-NHC complex against Aspergillus flavus (A. flavus) fungus was investigated with disc diffusion assay. Moreover, the biochemical changes occurring in this fungus due to exposure of different concentrations of the in-house synthesized compound are characterized by surface-enhanced Raman spectroscopy (SERS) and are illustrated in the form of SERS spectral peaks. SERS analysis yields valuable information about the probable mechanisms responsible for the antifungal effects of the Se-NHC complex. As demonstrated by the SERS spectra, this Se-NHC complex caused denaturation and conformational changes in the proteins as well as decomposition of the fungal cell membrane. The SERS spectra were analyzed using two chemometric tools such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). The fungal samples' SERS spectra were differentiated using PCA, while various groups of spectra were discriminated with ultrahigh sensitivity (98%), high specificity (99.7%), accuracy (100%), and area under the receiver operating characteristic curve (87%) using PLS-DA.

Qualitative and Quantitative Analysis of Azithromycin as Solid Dosage by Raman Spectroscopy.

Active pharmaceutical ingredients (APIs) and excipients are main drug constituents that ought to be identified qualitatively and quantitatively. Raman spectroscopy is aimed to be an efficient technique for pharmaceutical analysis in solid dosage forms. This technique can successfully be used in terms of qualitative and quantitative analysis of pharmaceutical drugs, their APIs, and excipients. In the proposed research, Raman spectroscopy has been employed to quantify Azithromycin based on its distinctive Raman spectral features by using commercially prepared formulations with altered API concentrations and excipients as well. Along with Raman spectroscopy, principal component analysis and partial least squares regression (PLSR), two multivariate data analysis techniques have been used for the identification and quantification of the API. For PLSR, goodness of fit of the model (R2) was found to be 0.99, whereas root mean square error of calibration was 0.46 and root mean square error of prediction was 2.42, which represent the performance of the model. This study highlights the efficiency of Raman spectroscopy in the field of pharmaceutics by preparing pharmaceutical formulations of any drug to quantify their API and excipients to compensate for the commercially prepared concentrations.

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.

Surface-enhanced Raman spectroscopy for monitoring antibacterial activity of imidazole derivative (1-benzyl-3-(sec­butyl)-1H-imidazole-3-ium bromide) against Bacillus subtilis and Escherichia coli.

BACKGROUND: Bacterial resistance against antibiotics remains a challenge and Raman Spectroscopy (SERS) may provide critical information concerning this. OBJECTIVES: In the current study, surface enhances Raman spectroscopy (SERS) has been used to determine the biochemical changes induced during the antibacterial activity of the in house synthesized imidazole derivative (1-benzyl-3-(sec­butyl)-1H-imidazole-3-ium bromide) in comparison to commercially available drugs (fasygien) against both gram-positive and gram-negative bacteria. METHODS: For this purpose, the antibacterial activity of this compound was assessed on Bacillus subtilis and Escherichia coli. The SERS spectral changes are detected which can be associated with the biochemical changes in the bacterial cells as a result of the application of both drugs, including fasygien and the imidazole derivative drug demonstrating the technique's potential for analyzing the antibacterial activities of drug candidates. RESULTS: The chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were performed for the differentiation of SERS spectral data sets of unexposed, exposed with imidazole derivative and commercially available antibacterial drugs for two different bacteria including E. coli and Bacillus. CONCLUSIONS: PCA was found helpful for the qualitative differentiation of all drug-treated E. coli and Bacillus in the form of separate clusters of spectral data sets and PLS-DA discriminated the unexposed and the exposed bacteria with imidazole derivative and commercially available drug with 93% sensitivity and 96% specificity for Bacillus and with 90% sensitivity and 89% specificity for E. coli.

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.

Surface-enhanced Raman spectroscopy of centrifuged blood serum samples of diabetic type II patients by using 50KDa filter devices.

Blood serum contains essential biochemical information which are used for early disease diagnosis. Blood serum consisted of higher molecular weight fractions (HMWF) and lower molecular weight fractions (LMWF). The disease biomarkers are lower molecular weight fraction proteins, and their contribution to disease diagnosis is suppressed due to higher molecular weight fraction proteins. To diagnose diabetes in early stages are difficult because of the presence of huge amount of these HMWF. In the current study, surface-enhanced Raman spectroscopy (SERS) are employed to diagnose diabetes after centrifugation of serum samples using Amicon ultra filter devices of 50 kDa which produced two fractions of whole blood serum of filtrate, low molecular weight fraction, and residue, high molecular weight fraction. Furthermore SERS is employed to study the LMW fractions of healthy and diseased samples. Some prominent SERS bands are observed at 725 cm-1, 842 cm-1, 1025 cm-1, 959 cm-1, and 1447 cm-1 due to small molecular weight proteins, and these biomarkers helped to diagnose the disease early stage. Moreover, chemometric techniques such as principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) are employed to check the potential of surface-enhanced Raman spectroscopy for the differentiation and classifications of the blood serum samples. SERS can be employed for the early diagnosis and screening of biochemical changes during type II diabetes.

Surface-enhanced Raman spectroscopy (SERS) for the characterization of supernatants of bacterial cultures of bacterial strains causing sinusitis.

BACKGROUND: Sinusitis is defined as inflammation of the paranasal sinus mucous membrane lining caused by bacteria which usually invade the sinus by upper respiratory tract viral infections (UTI). OBJECTIVES: In the present study, Surface-enhanced Raman spectroscopy (SERS) has been applied to differentiate and characterize supernatant samples, in triplicate, of three different types of bacteria which are considered leading cause of sinusitis disease. METHODS: For this purpose, supernatant samples of three different strains of bacteria namely Staphylococcus aureus, Klebsiella pneumoniae and Enterococcus faecalis. The SERS has identified significant changes as a result of secretions of biomolecules by these bacteria in their supernatants which can be helpful to explore the potential of this technique for the identification and characterization of different strains of bacteria causing same disease. RESULTS: These differentiating characteristic SERS spectral features including 552 cm-1 (C-S-S-C bonds), 951 cm-1 (CN stretching), 1008 cm-1 (Phenylalanine), 1032 cm-1 (In plane CH bending mode Phenylalanine), 1280 cm-1, 1320 cm-1, 1329 cm-1 (Amide III band), 1368 cm-1, 1400 cm-1, 1420 cm-1 (COO-sym. stretching and CH bending), 1583 cm-1 (Tyrosine) correspond to Proteins and 1051 cm-1 (C-C, C-O, -C-OH def.) correspond to carbohydrates contents of these three different types of bacterial secretions in their respective supernatants. Furthermore, multivariate data analysis techniques like principal component analysis (PCA) and a supervised method partial least squares-discriminant analysis (PLS-DA) were found to be useful for the identification and characterization of different bacterial supernatants. CONCLUSIONS: Surface-enhanced Raman spectroscopy is proven to be a helpful approach for the characterization and discrimination of three bacterial supernatants including S. aureus, K. pneumonia and E. faecalis.

Surface-enhanced Raman spectroscopy for studying the interaction of N-propyl substituted imidazole compound with salmon sperm DNA.

BACKGROUND: Surface Enhanced Raman Spectroscopy (SERS) is a very promising and fast technique for studying drugs and for detecting chemical nature of a molecule and DNA interaction. In the current study, SERS is employed to check the interaction of different concentrations of n-propyl imidazole derivative ligand with salmon sperm DNA using silver nanoparticles as SERS substrates. OBJECTIVES: Multivariate data analysis technique like principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) are employed for the detailed analysis of the SERS spectral features associated with the mode of action of the imidazole derivative ligand with DNA. METHODOLOGY: Silver nanoparticles were used as a SERS substrate in DNA-drug interaction. Five different concentrations of ligands were interacted with DNA and mix with Ag-NPs as substrate. The SERS spectra of were acquired for all seven samples and processed using MATLAB. Additionally, PCA and PLS-DA were used to assessed the ability SERS to differentiate interaction of DNA-drug. RESULTS: Differentiating SERS features having changes in their peak position and intensities are observed including 629, 655, 791, 807, 859, 1337, 1377 and 1456 cm-1. These SERS features reveal that binding of ligand with DNA is electrostatic in nature, and have specificity to major groove where it forms GC-CG interstrand cross-linking with the DNA double helix. CONCLUSIONS: SERS give significant information regarding to Drug-DNA interaction mechanism, SERS spectra inferred the mode of action of anticancer compound that are imidazole in nature.

Surface-enhanced Raman spectral characterization of antifungal activity of selenium and zinc based organometallic compounds.

Surface-enhanced Raman spectroscopy (SERS) is used to identify the biochemical changes associated with the antifungal activities of selenium and zinc organometallic complexes against Aspergillus niger fungus. These biochemical changes identified in the form of SERS peaks can help to understand the mechanism of action of these antifungal agents which is important for development of new antifungal drugs. The SERS spectral changes indicate the denaturation and conformational changes of proteins and fungal cell wall decomposition in complex exposed fungal samples. The SERS spectra of these organometallic complexes exposed fungi are analyzed by using statistical tools like principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). PCA is employed to differentiate the SERS spectra of fungal samples exposed to ligands and complexes. The PLS-DA discriminated different groups of spectra with 99.8% sensitivity, 100% specificity, 98% accuracy and 86 % area under receiver operating characteristic (AUROC) curve.

Surface-enhanced Raman spectroscopy for characterization of filtrate portions of blood serum samples of typhoid patients.

BACKGROUND: Surface-enhanced Raman spectroscopy (SERS) is explored to design a rapid screening method for the characterization and diagnosis of typhoid fever by employing filtrate fractions of blood serum samples obtained by centrifugal filtration with 50 KDa filters. OBJECTIVES: The purpose of this study, to separate the filtrate portions of blood serum samples in this way contain proteins smaller than 50 kDa and removal of bigger size protein which allows to acquire the SERS spectral features of smaller proteins more effectively which are probably associated with typhoid disease. Disease caused by Salmonella typhi diagnose more effectively by using surface-enhanced Raman spectroscopy (SERS) and multivariate data analysis tools. METHODS: SERS was used as a diagnostic tool for typhoid fever by comparison between healthy and diseased samples. For this purpose, all the samples were analyzed by comparing their SERS spectral features. Over the spectral range of 400-1800cm-1, multivariate data analysis techniques such as Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) are applied to diagnose and differentiate different filtrate fractions of blood serum samples of patients of typhoid fever and healthy ones. RESULTS: By comparing SERS spectra of healthy filtrate with that of filtrate of typhoid sample, the SERS spectral features associated with disease development are identified including PCA is found to be efficient for the qualitative differentiation of all of the samples analyzed. Moreover, PLS-DA successfully identified and classified healthy and typhoid positive blood serum samples with 97 % accuracy, 99 % specificity, 91 % sensitivity and 0.78 area under the receiver operating characteristic (AUROC) curve. CONCLUSIONS: Surface enhanced Raman spectroscopy using silver nanoparticles SERS substrate, is found to be useful technique for the quick identification and evaluation of filtrate fractions of the blood serum samples of healthy and typhoid samples for disease diagnosis.

Surface-enhanced Raman spectroscopy for the characterization of pellets of biofilm forming bacterial strains of Staphylococcus epidermidis.

BACKGROUND: Surface-enhanced Raman spectroscopy (SERS) is an effective tool for identifying biofilm forming bacterial strains. Biofilm forming bacteria are considered a major issue in the health sector because they have strong resistance against antibiotics. Staphylococcus epidermidis is commonly present on intravascular devices and prosthetic joints, catheters and wounds. OBJECTIVES: To identify and characterize biofilm forming and non-biofilm forming bacterial strains, surface- enhanced Raman spectroscopy with principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) were used. METHODS: Surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles were employed for the analysis and characterization of biofilm forming bacterial strains. SERS is used to differentiate between non biofilm forming (five samples), medium biofilm forming (five samples) and strong biofilm forming (five samples) bacterial strains by applying silver nanoparticles (AgNPs) as SERS substrate. Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) were used to discriminate between non, medium and strong biofilm ability of bacterial strains. RESULTS: Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been used to identify the biochemical differences in the form of SERS features which can be used to differentiate between biofilm forming and non-biofilm forming bacterial strains. PLS-DA provides successful differentiation and classification of these different strains with 94.5% specificity, 96% sensitivity and 89% area under the curve (AUC). CONCLUSIONS: Surface-enhanced Raman spectroscopy can be utilized to differentiate between non, medium and strong biofilm forming bacterial strains.

Surface-enhanced Raman spectroscopic analysis of centrifugally filtered blood serum samples of hepatitis C patients.

BACKGROUND: Raman spectroscopy is able to analyze non-invasively, disease related to body fluids. OBJECTIVES: For the qualitative and quantitative analysis of HCV serum samples surface-enhanced Raman spectroscopy (SERS) based method is developed. METHOD: Surface-enhanced Raman spectroscopy (SERS) technique is employed for analysis of filtrate portions of blood serum samples of hepatitis C virus (HCV) infected patients and healthy ones by using 50 kDa centrifugal filter device. The filtrate portions of the serum obtained in this way contain proteins smaller than 50 kDa and removal of bigger size protein which allows to acquire SERS spectral features of smaller proteins more effectively which are probably associated with Hepatitis C infection. Moreover, SERS spectral features of the filtrates of different level of viral load including low, medium and high viral loads are compared with SERS spectral features of the filtrate portions of healthy/control serum samples. SERS spectral data sets of different samples are further analyzed by using multivariate data analysis techniques such as principal component analysis (PCA) and partial least square regression (PLSR). Some SERS spectral features are solely observed in the filtrate portions of the serum samples of hepatitis C and their intensities are increased as the level of viral load increases and might be used for HCV diagnosis. RESULTS: PCA was found helpful for differentiation of SERS spectral data sets of filtrate portions of the serum samples of hepatitis C and healthy persons. The PLSR model helped for the quantification of viral loads in the unknown serum samples with 99% accuracy.

Surface-enhanced Raman spectroscopy (SERS) for monitoring colistin-resistant and susceptible E. coli strains.

The emergence of drug-resistant bacteria is a precarious global health concern. In this study, surface-enhanced Raman spectroscopy (SERS) is used to characterize colistin-resistant and susceptible E. coli strains based on their distinguished SERS spectral features for the development of rapid and cost-effective detection and differentiation methods. For this purpose, three colistin-resistant and three colistin susceptible E. coli strains were analyzed by comparing their SERS spectral signatures. Moreover, multivariate data analysis techniques including Principal component analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were used to examine the SERS spectral data of colistin-resistant and susceptible strains. PCA technique was employed for differentiating colistin susceptible and resistant E.coli strains due to alteration in biochemical compositions of the bacterial cell. PLS-DA is employed on SERS spectral data sets for discrimination of these resistant and susceptible E. coli strains with 100% specificity, 100% accuracy, 99.8% sensitivity, and 86% area under receiver operating characteristics (AUROC) curve.