Raman spectroscopic analysis of human blood serum of glaucoma patients supplemented with macular pigment carotenoids

As all major dietary carotenoids are contained in blood, it is a suitable substrate to evaluate their content, in vivo. Following 18-month supplementation of open-angle glaucoma patients with macula-pigment carotenoids (Lutein, Zeaxanthin and Meso-Zeaxanthin) in the European Nutrition in Glaucoma Management trial, Raman spectroscopic analysis of the carotenoid content of pre- and post-supplementation participant blood serum was carried out, to investigate the systemic impact of the supplementation regimen and explore a more direct way of quantifying this impact using routine blood tests. Using a 532 nm laser source for optimal response, a consistent increase in serum carotenoid concentration was observed in the supplemented serum, highest in patients with initial high baseline carotenoid content. A shift in the 1519 cm-1 carotenoid peak also revealed differences in the carotenoid structural profile of the two groups. The findings highlight the potential of Raman spectroscopy toquantify and differentiate carotenoids directly in blood serum.

Comprehensive spectroscopic, metabolomic, and proteomic liquid biopsy in the diagnostics of hepatocellular carcinoma

Liquid biopsy is a very topical issue in clinical diagnostics research nowadays. In this study, we explored and compared various analytical approaches to blood plasma analysis. Finally, we proposed a comprehensive procedure, which, thanks to the utilization of multiple analytical techniques, allowed the targeting of various biomolecules in blood plasma reflecting diverse biological processes underlying disease development. The potential of such an approach, combining proteomics, metabolomics, and vibrational spectroscopy along with preceding blood plasma fractionation, was demonstrated on blood plasma samples of patients suffering from hepatocellular carcinoma in cirrhotic terrain (n = 20) and control subjects with liver cirrhosis (n = 20) as well as healthy subjects (n = 20). Most of the applied methods allowed the classification of the samples with an accuracy exceeding 80.0 % and therefore have the potential to be used as a stand-alone method in clinical diagnostics. Moreover, a final panel of 48 variables obtained by a combination of the utilized analytical methods enabled the discrimination of the hepatocellular carcinoma samples from cirrhosis with 94.3 % cross-validated accuracy. Thus, this study, although limited by the cohort size, clearly demonstrated the benefit of the multimethod approach in clinical diagnosis.

Characterization of biomarkers in blood serum for cancer diagnosis in dogs using Raman spectroscopy

Biomarkers of cancer in sera of domestic dogs were detected through Raman spectroscopy with 830 nm excitation. Raman spectra of sera from 61 dogs (31 healthy and 30 with cancer, resulting in 154 and 200 spectra, respectively) were submitted to principal component analysis (PCA) for feature extraction and partial least squares (PLS) regression for discrimination between Healthy and Cancer groups. In the PCA, the peaks at 1132, 1342, 1368, and 1453 cm-1 (albumin and phenylalanine) were higher for the Cancer group. The "redshift" of the peaks at 621, 1003, and 1032 cm-1 (conformational change in proteins and/or bonds at sites close to the aromatic ring of amino acids) occurred in the Cancer group, and the peaks at 451 cm-1 (tryptophan) and 1441 cm-1 (lipids) were higher for the Healthy group. The PLS-DA classified the serum spectra in Healthy and Cancer groups with high accuracy (78%).

Raman Spectroscopy and Cystic Fibrosis Disease: An Alternative Potential Tool for Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) Modulator Response Differentiation-A Pilot Study Based on Serum Samples

Cystic fibrosis (CF) is a genetic disorder that alters chloride transport in mucous membranes. Recent studies have demonstrated that treatment with modulators of the chloride channel reduces inflammatory markers, restoring, among others, the imbalance of lipids. In this study, we analyzed the serum samples of treated and non-treated patients with modulators with Raman spectroscopy. Nineteen (eight treated an eleven non-treated) patients were considered. The main difference between the two groups appeared in the 3020-2800 cm-1 range. A Voigt deconvolution fit was performed, and nine sub-bands were identified. To distinguish between treated and non-treated patients, the area ratio between the CH3 and CH2 vibration modes was calculated for each patient. The results were validated using statistical analyses. In particular, receiver operating characteristic (ROC) curves and Youden index (Y) were calculated (Area Under Curve (AUC): 0.977; Y: 3.30). An ROC curve represents the performance of the classification, illustrating the diagnostic ability of Raman spectroscopy. It was demonstrated that Raman spectroscopy is able to highlight peculiar differences between elexacaftor/tezacaftor/ivacaftor (ETI)-treated and non-treated patients, in relation with lipids biomarkers.

Comparative analysis of serum and saliva samples using Raman spectroscopy: a high-throughput investigation in patients with polycystic ovary syndrome and periodontitis

BACKGROUND

Polycystic ovary syndrome (PCOS) and periodontitis significantly affect women's oral and systemic health worldwide, and yet increase the risk of cardiovascular-metabolic diseases like diabetes and coronary heart disease. Regarding the PCOS-periodontitis connection, whether sex hormones, metabolic and inflammatory mediators could account for the underlying linking mechanism needs to be further investigated. This case-control study evaluated the hormonal, metabolic and inflammatory profiles in PCOS and non-PCOS subjects with various periodontal conditions, via assessing serum and saliva samples by Raman spectroscopy.

METHODS

A total of 66 females with PCOS and 22 systemically healthy female volunteers were recruited in a single hospital. Full-mouth periodontal examination was undertaken for identifying the subjects with periodontal health, gingivitis or periodontitis. The datasets of sex hormones and metabolic indicators were retrieved from the hospital information system. Both serum and saliva samples were collected for detecting inflammatory mediators and Raman spectroscopic assessment. The subjects were categorized into four groups according to their conditions of PCOS and periodontitis for Raman spectroscopic analysis. Partial least squares discriminant analysis was performed to examine the inter-group differences in Raman spectra.

RESULTS

PCOS patients exhibited greater mean probing depth (P < 0.05) and higher serum levels of triglycerides (P < 0.05) and matrix metalloproteinase-8 (P < 0.05) than those in non-PCOS participants. Both probing depth and triglyceride level were positively correlated with PCOS (P < 0.05). There was a significant difference in mean Raman spectra of saliva samples among the four groups with different conditions of PCOS and periodontitis (P < 0.05), while no significant inter-group difference existed in serum samples.

CONCLUSIONS

The present study shows that periodontal condition may affect the biomolecular profiles of Raman spectra in serum and saliva of PCOS patients. It underscores the importance of the collaborative teamwork of dentists and gynecologists for enhancing women's oral health, general wellbeing and quality of life.

Label-Free Quantification of Nanoencapsulated Piperonyl Esters in Cosmetic Hydrogels Using Raman Spectroscopy

Raman spectroscopy is a well-established technique for the molecular characterisation of samples and does not require extensive pre-analytical processing for complex cosmetic products. As an illustration of its potential, this study investigates the quantitative performance of Raman spectroscopy coupled with partial least squares regression (PLSR) for the analysis of Alginate nanoencapsulated Piperonyl Esters (ANC-PE) incorporated into a hydrogel. A total of 96 ANC-PE samples covering a 0.4% w/w-8.3% w/w PE concentration range have been prepared and analysed. Despite the complex formulation of the sample, the spectral features of the PE can be detected and used to quantify the concentrations. Using a leave-K-out cross-validation approach, samples were divided into a training set (n = 64) and a test set, samples that were previously unknown to the PLSR model (n = 32). The root mean square error of cross-validation (RMSECV) and prediction (RMSEP) was evaluated to be 0.142% (w/w PE) and 0.148% (w/w PE), respectively. The accuracy of the prediction model was further evaluated by the percent relative error calculated from the predicted concentration compared to the true value, yielding values of 3.58% for the training set and 3.67% for the test set. The outcome of the analysis demonstrated the analytical power of Raman to obtain label-free, non-destructive quantification of the active cosmetic ingredient, presently PE, in complex formulations, holding promise for future analytical quality control (AQC) applications in the cosmetics industry with rapid and consumable-free analysis.

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.

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.

Discrimination of leukemias and non-leukemic cancers in blood serum samples of children and adolescents using a Raman spectral model

This study aimed to identify the differences presented in the Raman spectrum of blood serum from normal subjects compared to leukemic and non-leukemic subjects and the differences between the leukemics and non-leukemics, correlating the spectral differences with the biomolecules. Serum samples from children and adolescents were subjected to Raman spectroscopy (830 nm, laser power 350 mW; n = 566 spectra, being 72 controls, 269 leukemics, and 225 non-leukemics). Exploratory analysis based on principal component analysis (PCA) of the serum sample's spectra was performed. Classification models based on partial least squares discriminant analysis (PLS-DA) were developed to classify the spectra into normal, leukemic, and non-leukemic, as well as to discriminate spectra of leukemic from non-leukemic. The exploratory analysis showed principal components with peaks related to amino acids, proteins, lipids, and carotenoids. The spectral differences between normal, leukemic, and non-leukemic showed features assigned to proteins (serum features), amino acids, and carotenoids. The PLS-DA model classified the spectra of the normal group versus leukemic and non-leukemic groups with accuracy of 66%, sensitivity of 99%, and specificity of 57%. The PLS-DA discriminated the spectra of the leukemic and non-leukemic groups with accuracy of 67%, sensitivity of 72%, and specificity of 60%. The study showed that Raman spectroscopy is a technique that may be used for the biochemical differentiation of leukemias and other types of cancer in serum samples of children and adolescents. Nevertheless, building an extensive data library of Raman spectra from serum samples of controls, leukemics, and non-leukemics of different age groups is necessary to understand the findings better.

Raman Spectroscopy of Carotenoid Compounds for Clinical Applications-A Review

Carotenoid compounds are ubiquitous in nature, providing the characteristic colouring of many algae, bacteria, fruits and vegetables. They are a critical component of the human diet and play a key role in human nutrition, health and disease. Therefore, the clinical importance of qualitative and quantitative carotene content analysis is increasingly recognised. In this review, the structural and optical properties of carotenoid compounds are reviewed, differentiating between those of carotenes and xanthophylls. The strong non-resonant and resonant Raman spectroscopic signatures of carotenoids are described, and advances in the use of Raman spectroscopy to identify carotenoids in biological environments are reviewed. Focus is drawn to applications in nutritional analysis, optometry and serology, based on in vitro and ex vivo measurements in skin, retina and blood, and progress towards establishing the technique in a clinical environment, as well as challenges and future perspectives, are explored.

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.

The Potential Applications of Raman Spectroscopy in Kidney Diseases

Raman spectroscopy (RS) is a spectroscopic technique based on the inelastic interaction of incident electromagnetic radiation (from a laser beam) with a polarizable molecule, which, when scattered, carries information from molecular vibrational energy (the Raman effect). RS detects biochemical changes in biological samples at the molecular level, making it an effective analytical technique for disease diagnosis and prognosis. It outperforms conventional sample preservation techniques by requiring no chemical reagents, reducing analysis time even at low concentrations, and working in the presence of interfering agents or solvents. Because routinely utilized biomarkers for kidney disease have limitations, there is considerable interest in the potential use of RS. RS may identify and quantify urinary and blood biochemical components, with results comparable to reference methods in nephrology.

Quantitative Raman Analysis of Carotenoid Protein Complexes in Aqueous Solution

Carotenoids are naturally abundant, fat-soluble pigmented compounds with dietary, antioxidant and vision protection advantages. The dietary carotenoids, Beta Carotene, Lutein, and Zeaxanthin, complexed with in bovine serum albumin (BSA) in aqueous solution, were explored using Raman spectroscopy to differentiate and quantify their spectral signatures. UV visible absorption spectroscopy was employed to confirm the linearity of responses over the concentration range employed (0.05–1 mg/mL) and, of the 4 Raman source wavelengths (785 nm, 660 nm, 532 nm, 473 nm), 532 nm was chosen to provide the optimal response. After preprocessing to remove water and BSA contributions, and correct for self-absorption, a partial least squares model with R2 of 0.9995, resulted in an accuracy of the Root Mean Squared Error of Prediction for Beta Carotene of 0.0032 mg/mL and Limit of Detection 0.0106 mg/mL. Principal Components Analysis clearly differentiated solutions of the three carotenoids, based primarily on small shifts of the main peak at ~1520 cm−1. Least squares fitting analysis of the spectra of admixtures of the carotenoid:protein complexes showed reasonable correlation between norminal% and fitted%, yielding 100% contribution when fitted with individual carotenoid complexes and variable contributions with multiple ratios of admixtures. The results indicate the technique can potentially be used to quantify the carotenoid content of human serum and to identify their differential contributions for application in clinical analysis.

A Combined Near-Infrared and Mid-Infrared Spectroscopic Approach for the Detection and Quantification of Glycine in Human Serum

Serum is an important candidate in proteomics analysis as it potentially carries key markers on health status and disease progression. However, several important diagnostic markers found in the circulatory proteome and the low-molecular-weight (LMW) peptidome have become analytically challenging due to the high dynamic concentration range of the constituent protein/peptide species in serum. Herein, we propose a novel approach to improve the limit of detection (LoD) of LMW amino acids by combining mid-IR (MIR) and near-IR spectroscopic data using glycine as a model LMW analyte. This is the first example of near-IR spectroscopy applied to elucidate the detection limit of LMW components in serum; moreover, it is the first study of its kind to combine mid-infrared (25-2.5 μm) and near-infrared (2500-800 nm) to detect an analyte in serum. First, we evaluated the prediction model performance individually with MIR (ATR-FTIR) and NIR spectroscopic methods using partial least squares regression (PLS-R) analysis. The LoD was found to be 0.26 mg/mL with ATR spectroscopy and 0.22 mg/mL with NIR spectroscopy. Secondly, we examined the ability of combined spectral regions to enhance the detection limit of serum-based LMW amino acids. Supervised extended wavelength PLS-R resulted in a root mean square error of prediction (RMSEP) value of 0.303 mg/mL and R² value of 0.999 over a concentration range of 0-50 mg/mL for glycine spiked in whole serum. The LoD improved to 0.17 mg/mL from 0.26 mg/mL. Thus, the combination of NIR and mid-IR spectroscopy can improve the limit of detection for an LMW compound in a complex serum matrix.

Diagnosing COVID-19 in human serum using Raman spectroscopy

This study proposed the diagnosis of COVID-19 by means of Raman spectroscopy. Samples of blood serum from 10 patients positive and 10 patients negative for COVID-19 by RT-PCR RNA and ELISA tests were analyzed. Raman spectra were obtained with a dispersive Raman spectrometer (830 nm, 350 mW) in triplicate, being submitted to exploratory analysis with principal component analysis (PCA) to identify the spectral differences and discriminant analysis with PCA (PCA-DA) and partial least squares (PLS-DA) for classification of the blood serum spectra into Control and COVID-19. The spectra of both groups positive and negative for COVID-19 showed peaks referred to the basal constitution of the serum (mainly albumin). The difference spectra showed decrease in the peaks referred to proteins and amino acids for the group positive. PCA variables showed more detailed spectral differences related to the biochemical alterations due to the COVID-19 such as increase in lipids, nitrogen compounds (urea and amines/amides) and nucleic acids, and decrease of proteins and amino acids (tryptophan) in the COVID-19 group. The discriminant analysis applied to the principal component loadings (PC2, PC4, PC5, and PC6) could classify spectra with 87% sensitivity and 100% specificity compared to 95% sensitivity and 100% specificity indicated in the RT-PCR kit leaflet, demonstrating the possibilities of a rapid, label-free, and costless technique for diagnosing COVID-19 infection.

Optimised Pre-Processing of Raman Spectra for Colorectal Cancer Detection Using High-Performance Computing

Spectral pre-processing is an essential step in data analysis for biomedical diagnostic applications of Raman spectroscopy, allowing the removal of undesirable spectral contributions that could mask biological information used for diagnosis. However, due to the specificity of pre-processing for a given sample type and the vast number of potential pre-processing combinations, optimisation of pre-processing via a manual "trial and error" format is often time intensive with no guarantee that the chosen method is optimal for the sample type. Here we present the use of high-performance computing (HPC) to trial over 2.4 million pre-processing permutations to demonstrate the optimisation on the pre-processing of human serum Raman spectra for colorectal cancer detection. The effect of varying pre-processing order, using extended multiplicative scatter correction, spectral smoothing, baseline correction, binning and normalization was considered. Permutations were assessed on their ability to detect patients with disease using a random forest (RF) algorithm trained with 102 patients (510 spectra) and independently tested with a set of 439 patients (1317 spectra) in a primary care patient cohort. Optimising via HPC enables improved performance in diagnostic abilities, with sensitivity increasing by 14.6%, specificity increasing by 6.9%, positive predictive value increasing by 3.4%, and negative predictive value increasing by 2.4% when compared to a standard pre-processing optimisation. Ultimate values of these metrics are very important for diagnostic adoption, and once diagnostics demonstrate good accuracy these types of optimisations can make a significant difference to roll-out of a test and demonstrating advantages over existing tests. We also provide tips/recommendations for pre-processing optimisation without the use of HPC. From the HPC permutations, recommendations for appropriate parameter constraints for conducting a more basic pre-processing optimisation are also detailed, thus helping model development for researchers not having access to HPC.

Surface-enhanced Raman spectroscopic analysis of centrifugally filtered HBV serum samples

BACKGROUND

Raman spectroscopy is an effective tool for detecting and discriminating centrifugally filtered hepatitis B virus serum and centrifugally filtered  control serum.

OBJECTIVES

The purpose of current study is to separate high molecular weight fractions from low molecular weight fractions present hepatitis B serum to increase the disease diagnostic ability of surface enhanced Raman spectroscopy (SERS).

METHODS

Clinically diagnosed centrifugally filtered serum samples of hepatitis B patients are subjected for surface enhanced Raman spectroscopy (SERS) in comparison with centrifugally filtered serum samples of healthy individuals by using silver nanoparticles (Ag-NPs) as SERS substrates. Some SERS spectral features are solely observed in centrifugally filtered serum samples of hepatitis B and some SERS spectral are solely observed in centrifugally filtered serum samples of healthy individuals. The diagnostic ability of SERS is further enhanced with different statistical techniques like principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and partial least square regression analysis (PLSR) have applied.

RESULTS

The disease biomarkers of hepatitis B are more pronounced after their centrifugation as compared with uncentrifuged form. Statistical tools like principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) clearly differentiated centrifugally filtered serum samples of hepatitis B from centrifugally filtered serum samples of healthy individuals. Furthermore, partial least square regression analysis (PLSR) has been applied for predicting unknown viral load of centrifugally filtered serum sample of hepatitis B.

CONCLUSION

SERS technique along with chemometric tools have successfully differentiated centrifugally filtered serum samples of hepatitis B from centrifugally filtered serum samples of healthy individuals. The centrifugal filtration process has increased the differentiation accuracy of PLS-DA in terms of percentage 98% and regression accuracy of PLSR regression analysis in terms of RMSEP (0.30 IU/mL) of this diagnostic method as compared with that of uncentrifuged method.

Diagnosing COVID-19 in human serum using Raman spectroscopy

This study proposed the diagnosis of COVID-19 by means of Raman spectroscopy. Samples of blood serum from 10 patients positive and 10 patients negative for COVID-19 by RT-PCR RNA and ELISA tests were analyzed. Raman spectra were obtained with a dispersive Raman spectrometer (830 nm, 350 mW) in triplicate, being submitted to exploratory analysis with principal component analysis (PCA) to identify the spectral differences and discriminant analysis with PCA (PCA-DA) and partial least squares (PLS-DA) for classification of the blood serum spectra into Control and COVID-19. The spectra of both groups positive and negative for COVID-19 showed peaks referred to the basal constitution of the serum (mainly albumin). The difference spectra showed decrease in the peaks referred to proteins and amino acids for the group positive. PCA variables showed more detailed spectral differences related to the biochemical alterations due to the COVID-19 such as increase in lipids, nitrogen compounds (urea and amines/amides) and nucleic acids, and decrease of proteins and amino acids (tryptophan) in the COVID-19 group. The discriminant analysis applied to the principal component loadings (PC2, PC4, PC5, and PC6) could classify spectra with 87% sensitivity and 100% specificity compared to 95% sensitivity and 100% specificity indicated in the RT-PCR kit leaflet, demonstrating the possibilities of a rapid, label-free, and costless technique for diagnosing COVID-19 infection.

Diagnosing COVID-19 in human sera with detected immunoglobulins IgM and IgG by means of Raman spectroscopy

The severe COVID-19 pandemic requires the development of novel, rapid, accurate, and label-free techniques that facilitate the detection and discrimination of SARS-CoV-2 infected subjects. Raman spectroscopy has been used to diagnose COVID-19 in serum samples of suspected patients without clinical symptoms of COVID-19 but presented positive immunoglobulins M and G (IgM and IgG) assays versus Control (negative IgM and IgG). A dispersive Raman spectrometer (830 nm, 350 mW) was employed, and triplicate spectra were obtained. A total of 278 spectra were used from 94 serum samples (54 Control and 40 COVID-19). The main spectral differences between the positive IgM and IgG versus Control, evaluated by principal component analysis (PCA), were features assigned to proteins including albumin (lower in the group COVID-19 and in the group IgM/IgG and IgG positive) and features assigned to lipids, phospholipids, and carotenoids (higher the group COVID-19 and in the group IgM/IgG positive). Features referred to nucleic acids, tryptophan, and immunoglobulins were also seen (higher the group COVID-19). A discriminant model based on partial least squares regression (PLS-DA) found sensitivity of 84.0%, specificity of 95.0%, and accuracy of 90.3% for discriminating positive Ig groups versus Control. When considering individual Ig group versus Control, it was found sensitivity of 77.3%, specificity of 97.5%, and accuracy of 88.8%. The higher classification error was found for the IgM group (no success classification). Raman spectroscopy may become a technique of choice for rapid serological evaluation aiming COVID-19 diagnosis, mainly detecting the presence of IgM/IgG and IgG after COVID-19 infection.

Molecular Origin of Blood-Based Infrared Spectroscopic Fingerprints*

Infrared spectroscopy of liquid biopsies is a time- and cost-effective approach that may advance biomedical diagnostics. However, the molecular nature of disease-related changes of infrared molecular fingerprints (IMFs) remains poorly understood, impeding the method's applicability. Here we probe 148 human blood sera and reveal the origin of the variations in their IMFs. To that end, we supplemented infrared spectroscopy with biochemical fractionation and proteomic profiling, providing molecular information about serum composition. Using lung cancer as an example of a medical condition, we demonstrate that the disease-related differences in IMFs are dominated by contributions from twelve highly abundant proteins-that, if used as a pattern, may be instrumental for detecting malignancy. Tying proteomic to spectral information and machine learning advances our understanding of the infrared spectra of liquid biopsies, a framework that could be applied to probing of any disease.

In situ Analytical Quality Control of chemotherapeutic solutions in infusion bags by Raman spectroscopy

Analytical Quality Control (AQC) in centralised preparation units of oncology centers is a common procedure relying on the identification and quantification of the prepared chemotherapeutic solutions for safe intravenous administration to patients. Although the use of Raman spectroscopy for AQC has gained much interest, in most applications it remains coupled to a flow injection analyser (FIA) requiring withdrawal of the solution for analysis. In addition to current needs for more rapid and cost-effective analysis, the risk of exposure of clinical staff to the toxic molecules during daily handling is a serious concern to address. Raman spectroscopic analysis, for instance by Confocal Raman Microscopy (CRM), could enable direct analysis (non-invasive) for AQC directly in infusion bags. In this study, 3 anticancer drugs, methotrexate (MTX), 5-fluorouracil (5-FU) and gemcitabine (GEM) have been selected to highlight the potential of CRM for withdrawal free analysis. Solutions corresponding to the clinical range of each drug were prepared in 5% glucose and data was collected from infusion bags placed under the Raman microscope. Firstly, 100% discrimination has been obtained by Partial Least Squares Discriminant Analysis (PLS-DA) confirming that the identification of drugs can be performed. Secondly, using Partial Least Squares Regression (PLSR), quantitative analysis was performed with mean % error of predicted concentrations of respectively 3.31%, 5.54% and 8.60% for MTX, 5-FU and GEM. These results are in accordance with the 15% acceptance criteria used for the current clinical standard technique, FIA, and the Limits of Detection for all drugs were determined to be substantially lower than the administered range, thus highlighting the potential of confocal Raman spectroscopy for direct analysis of chemotherapeutic solutions.

Biomedical applications of vibrational spectroscopy: Oral cancer diagnostics

Vibrational spectroscopy, based on either infrared absorption or Raman scattering, has attracted increasing attention for biomedical applications. Proof of concept explorations for diagnosis of oral potentially malignant disorders and cancer are reviewed, and recent advances critically appraised. Specific examples of applications of Raman microspectroscopy for analysis of histological, cytological and saliva samples are presented for illustrative purposes, and the future prospects, ultimately for routine, chairside in vivo screening are discussed.

Analysis of Chemisorbed Tribo-Film for Ceramic-on-Ceramic Hip Joint Prostheses by Raman Spectroscopy

To understand the possible lubricant mechanism in ceramic-on-ceramic hip joint prostheses, biochemical reactions of the synovial fluid and the corresponding frictional coefficients were studied. The experiments were performed in a hip joint simulator using the ball-on-cup configuration with balls and cups made from two types of ceramics, BIOLOX^®forte and BIOLOX^®delta. Different lubricants, namely albumin, γ-globulin, hyaluronic acid and three model synovial fluids, were studied in the experiments and Raman spectroscopy was used to analyze the biochemical responses of these lubricants at the interface. BIOLOX^®delta surface was found less reactive to proteins and model fluid lubricants. In contrast, BIOLOX^®forte ball surface has shown chemisorption with both proteins, hyaluronic acid and model fluids imitating total joint replacement and osteoarthritic joint. There was no direct correlation between the measured frictional coefficient and the observed chemical reactions. In summary, the study reveals chemistry of lubricant film formation on ceramic hip implant surfaces with various model synovial fluids and their components.

Serum Raman spectroscopy combined with multiple algorithms for diagnosing thyroid dysfunction and chronic renal failure

In this study, 60 samples taken from patients with thyroid dysfunction, 40 samples taken from patients with chronic renal failure (CRF) and 60 samples taken from healthy people were classified. We used partial least squares (PLS) to extract features to reduce the dimension of the spectral data to discriminate among the different samples. The Decision Trees (DT), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN), Back Propagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) algorithms were used to build classification models and compare the results. The PLS-PNN algorithm distinguished between patients with thyroid dysfunction and patients with chronic renal failure with up to a 96.67 % accuracy rate, the PLS-BP algorithm distinguished between patients with chronic renal failure and healthy people with up to a 98.33 % accuracy rate, and the PLS-PNN algorithm and the PLS-DT algorithm distinguished between healthy people and patients with chronic renal failure with up to a 100 % accuracy rate. The results showed that serum Raman spectroscopy can be used in conjunction with classification algorithms to rapidly and accurately diagnose and distinguish between thyroid dysfunction and chronic renal failure.

Novel diagnostic and therapeutic techniques reveal changed metabolic profiles in recurrent focal segmental glomerulosclerosis

Idiopathic forms of Focal Segmental Glomerulosclerosis (FSGS) are caused by circulating permeability factors, which can lead to early recurrence of FSGS and kidney failure after kidney transplantation. In the past three decades, many research endeavors were undertaken to identify these unknown factors. Even though some potential candidates have been recently discussed in the literature, "the" actual factor remains elusive. Therefore, there is an increased demand in FSGS research for the use of novel technologies that allow us to study FSGS from a yet unexplored angle. Here, we report the successful treatment of recurrent FSGS in a patient after living-related kidney transplantation by removal of circulating factors with CytoSorb apheresis. Interestingly, the classical published circulating factors were all in normal range in this patient but early disease recurrence in the transplant kidney and immediate response to CytoSorb apheresis were still suggestive for pathogenic circulating factors. To proof the functional effects of the patient's serum on podocytes and the glomerular filtration barrier we used a podocyte cell culture model and a proteinuria model in zebrafish to detect pathogenic effects on the podocytes actin cytoskeleton inducing a functional phenotype and podocyte effacement. We then performed Raman spectroscopy in the < 50 kDa serum fraction, on cultured podocytes treated with the FSGS serum and in kidney biopsies of the same patient at the time of transplantation and at the time of disease recurrence. The analysis revealed changes in podocyte metabolome induced by the FSGS serum as well as in focal glomerular and parietal epithelial cell regions in the FSGS biopsy. Several altered Raman spectra were identified in the fractionated serum and metabolome analysis by mass spectrometry detected lipid profiles in the FSGS serum, which were supported by disturbances in the Raman spectra. Our novel innovative analysis reveals changed lipid metabolome profiles associated with idiopathic FSGS that might reflect a new subtype of the disease.

Quantitative analysis of human blood serum using vibrational spectroscopy

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State of the art of quantitative Vibrational Spectroscopic analysis of human blood serum is reviewed.

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Technical considerations for infrared absorption and Raman analysis are discussed.

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Quantitative analyses of Endogenous and Exogenous constituents are presented.

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The potential for clinical translation of spectroscopic serology is argued.

Analysis of bodily fluids using vibrational spectroscopy has attracted increasing attention in recent years. In particular, infrared spectroscopic screening of blood products, particularly blood serum, for disease diagnostics has been advanced considerably, attracting commercial interests. However, analyses requiring quantification of endogenous constituents or exogenous agents in blood are less well advanced. Recent advances towards this end are reviewed, focussing on infrared and Raman spectroscopic analyses of human blood serum. The importance of spectroscopic analysis in the native aqueous environment is highlighted, and the relative merits of infrared absorption versus Raman spectroscopy are considered, in this context. It is argued that Raman spectroscopic analysis is more suitable to quantitative analysis in liquid samples, and superior performance for quantification of high and low molecular weight components, is demonstrated. Applications for quantitation of viral loads, and therapeutic drug monitoring are also discussed.

Raman Analysis Reveals Biochemical Differences in Plasma of Crohn's Disease Patients

BACKGROUNDS AND AIMS

There is no accurate and reliable circulating biomarker to diagnose Crohn's disease [CD]. Raman spectroscopy is a relatively new approach that provides information on the biochemical composition of samples in minutes and virtually without any sample preparation. We aimed to test the use of Raman spectroscopy analysis of plasma samples as a potential diagnostic tool for CD.

METHODS

We analysed by Raman spectroscopy dry plasma samples obtained from 77 CD patients [CD] and 45 healthy controls [HC]. In the dataset obtained, we analysed spectra differences between CD and HC, as well as among CD patients with different disease behaviours. We also developed a method, based on principal component analysis followed by a linear discrimination analysis [PCA-LDA], for the automatic classification of individuals based on plasma spectra analysis.

RESULTS

Compared with HC, the CD spectra were characterised by less intense peaks corresponding to carotenoids [p <10-4] and by more intense peaks corresponding to proteins with β-sheet secondary structure [p <10-4]. Differences were also found on Raman peaks relative to lipids [p = 0.0007] and aromatic amino acids [p <10-4]. The predictive model we developed was able to classify CD and HC subjects with 83.6% accuracy [sensitivity 80.0% and specificity 85.7%] and F1-score of 86.8%.

CONCLUSIONS

Our results indicate that Raman spectroscopy of blood plasma can identify metabolic variations associated with CD and it could be a rapid pre-screening tool to use before further specific evaluation.

Raman spectroscopy as a potential tool for label free therapeutic drug monitoring in human serum: the case of busulfan and methotrexate

A methodology is proposed, based on Raman spectroscopy coupled with multivariate analysis, to determine the Limit of Detection (LOD) and Limit of Quantification (LOQ) for therapeutic drug monitoring in human serum, using the examples of Busulfan and Methotrexate.