Quantifying glucose and lipid components in human serum by Raman spectroscopy and multivariate statistics

Differences in whole blood before and after hemodialysis session of subjects with chronic kidney disease measured by Raman spectroscopy

This study aimed to identify differences in the composition of whole blood of patients with chronic kidney disease (CKD), before and after a hemodialysis session (HDS), and possible differences in blood composition between stages and between genders using Raman spectroscopy and principal component analysis (PCA). Whole blood samples were collected from 40 patients (20 women and 20 men), before and after a HDS. Raman spectra were obtained and the spectra were evaluated by PCA and partial least squares (PLS) regression. Mean spectra and difference spectrum between the groups were calculated: stages Before and After HDS, and gender Women and Men, which had their most intense peaks identified. Stage: mean spectra and difference spectrum indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin in the group Before HDS. There was no statistically significant difference by PCA. Gender: mean spectra and difference spectrum Before HDS indicated positive peaks that could be assigned to red blood cells, hemoglobin and deoxi-hemoglobin with greater intensity in the group Women, and negative peaks to white blood cells and serum, with greater intensity in the group Men. There was statistically significant difference by PCA, which also identified the peaks assigned to white blood cells, serum and porphyrin for Women and red blood cells and amino acids (tryptophan) for Men. PLS model was able to classify the spectra of the gender with 83.7% accuracy considering the classification per patient. The Raman technique highlighted gender differences in pacients with CKD.

Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening

Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput in vitro diagnostics. While several studies showcased its potential in detecting health conditions, a large-scale analysis of a naturally heterogeneous potential patient population has not been attempted. Using a population-based cohort, here we analyze 5,184 blood plasma samples from 3,169 individuals using Fourier transform infrared (FTIR) spectroscopy. Applying a multi-task classification to distinguish between dyslipidemia, hypertension, prediabetes, type 2 diabetes, and healthy states, we find that the approach can accurately single out healthy individuals and characterize chronic multimorbid states. We further identify the capacity to forecast the development of metabolic syndrome years in advance of onset. Dataset-independent testing confirms the robustness of infrared signatures against variations in sample handling, storage time, and measurement regimes. This study provides the framework that establishes infrared molecular fingerprinting as an efficient modality for populational health diagnostics.

A Raman spectral marker for the iso-octyl chain structure of cholesterol

Raman spectroscopy provides label-free, specific analysis of biomolecular structure and interactions. It could have a greater impact with improved characterization of complex fingerprint vibrations. Many Raman peaks have been assigned to cholesterol, for example, but the molecular vibrations associated with those peaks are not known. In this report, time-dependent density functional theory calculations of the Raman spectrum of cholesterol are compared to measurements on microcrystalline powder to identify 23 peaks in the Raman spectrum. Among them, a band of six peaks is found to be sensitive to the conformational structure of cholesterol's iso-octyl chain. Calculations on 10 conformers in this spectral band are fit to experimental spectra to probe the cholesterol chain structure in purified powder and in phospholipid vesicles. In vesicles, the chain is found to bend perpendicular to the steroid rings, supporting the case that the chain is a dynamic structure that contributes to lipid condensation and other effects of cholesterol in biomembranes. Statement of Significance: Here we use density functional theory to identify a band of six peaks in cholesterol's Raman spectrum that is sensitive to the conformational structure of cholesterol's chain. Raman spectra were analyzed to show that in fluid-phase lipid membranes, about half of the cholesterol chains point perpendicular to the steroid rings. This new method of label-free structural analysis could make significant contributions to our understanding of cholesterol's critical role in biomembrane structure and function. More broadly, the results show that computational quantum chemistry Raman spectroscopy can make significant new contributions to molecular structure when spectra are interpreted with computational quantum chemistry.

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.

Central and Peripheral Fatigue Evaluation during Physical Exercise in Athletic Horses by Means of Raman Spectroscopy

The evaluation of the performance levels in athletic horses is of major importance to prevent sports injuries. Raman spectroscopy is an innovative technique that allows for a rapid evaluation of biomolecules in biological fluids. It also permits qualitative and quantitative sample analyses, which lead to the simultaneous determination of the components of the examined biological fluids. On the basis of this, the Raman spectroscopy technique was applied on serum samples collected from five Italian Saddle horses subjected to a standardized obstacle course preceded by a warm-up to evaluate the applicability of this technique for the assessment of central and peripheral fatigue in athletic horses. Blood samples were collected via jugular venipuncture in a vacutainer tube with a clot activator before exercise, immediately after exercise, and 30 min and 1 h after the end of the obstacle course. Observing the obtained Raman spectra, the major changes due to the experimental conditions appeared in the (1300-1360) cm^-1 and (1385-1520) cm^-1 bands. In the (1300-1360) cm^-1 band, lipids and tryptophan were identified; in the (1385-1520) cm^-1 band, leucine, glycine, isoleucine, lactic acid, tripeptide, adenosine, and beta carotene were identified. A significant effect of exercise was recorded on all the sub-bands. In particular, a change immediately after exercise versus before exercise was found. Moreover, the mean lactic concentration was positively correlated with the Raman area of the sub-band assigned to lactic acid. In this context, the application of Raman spectroscopy on blood serum samples represents a useful technique for secondary-structure protein identification to investigate the metabolic changes that occur in athletic horses during physical exercise.

Label-free atherosclerosis diagnosis through a blood drop of apolipoprotein E knockout mouse model using surface-enhanced Raman spectroscopy validated by machine learning algorithm

The direct preventative detection of flow-induced atherosclerosis remains a significant challenge, impeding the development of early treatments and prevention measures. This study proposes a method for diagnosing atherosclerosis in the carotid artery using nanometer biomarker measurements through surface-enhanced Raman spectroscopy (SERS) from single-drop blood samples. Atherosclerotic acceleration is induced in apolipoprotein E knockout mice which underwent a partial carotid ligation and were fed a high-fat diet to rapidly induce disturbed flow-induced atherosclerosis in the left common carotid artery while using the unligated, contralateral right carotid artery as control. The progressive atherosclerosis development of the left carotid artery was verified by micro-magnetic resonance imaging (micro-MRI) and histology in comparison to the right carotid artery. Single-drop blood samples are deposited on chips of gold-coated ZnO nanorods grown on silicon wafers that filter the nanometer markers and provide strong SERS signals. A diagnostic classifier was established based on principal component analysis (PCA), which separates the resultant spectra into the atherosclerotic and control groups. Scoring based on the principal components enabled the classification of samples into control, mild, and severe atherosclerotic disease. The PCA-based analysis was validated against an independent test sample and compared against the PCA-PLS-DA machine learning algorithm which is known for applicability to Raman diagnosis. The accuracy of the PCA modification-based diagnostic criteria was 94.5%, and that of the machine learning algorithm 97.5%. Using a mouse model, this study demonstrates that diagnosing and classifying the severity of atherosclerosis is possible using a single blood drop, SERS technology, and machine learning algorithm, indicating the detectability of biomarkers and vascular factors in the blood which correlate with the early stages of atherosclerosis development.

Diagnosis of Ischemic Renal Failure Using Surface-Enhanced Raman Spectroscopy and a Machine Learning Algorithm

To diagnose renal function using a biochip capable of detecting SERS and to assess Raman measurements taken from a bilateral renal ischemia model and the feasibility of early diagnosis was done. After generating a bilateral renal ischemia rat model, blood and urine were collected. After confirming the presence of renal injury and function, liquid drops were placed onto a Raman chip whose surface had been enhanced with Au-ZnO nanorods. SERS biomarkers that diffused into the nanogaps were selectively amplified. Raman signals varied based on the severity of the renal function, and these differences were confirmed statistically. These results confirm that renal ischemia leads to renal dysfunction and that surface-enhanced Raman spectroscopy and a machine learning algorithm can be used to track signals in the urine from the release of SERS biomarkers.

Quantitative analysis of Raman spectra for glucose concentration in human blood using Gramian angular field and convolutional neural network

In this study, convolutional neural network based on Gramian angular field (GAF-CNN) was firstly proposed. The 1-D Raman spectral data was converted into images and used for predicting the biochemical value of blood glucose. 106 sets of blood spectrums were acquired by Fourier transform (FT) Raman spectroscopy. Spectral data ranging from 800 cm^-1 to 1800 cm^-1 were selected for quantitative analysis of the blood glucose. Data augmentation was used to train neural networks and normalize the Raman spectra. And, we applied principal component analysis (PCA) for dimension reduction and information extraction. The root mean squared error of prediction (RMSEP) are 0.06570 (GADF) and 0.06774 (GASF), the determination coefficient of prediction (R²) are 0.99929 (GADF) and 0.99925 (GASF), and the residual predictive deviation of prediction (RPD) are 37.56324 (GADF) and 36.43362 (GASF). GAF-CNN model performed better for predicting of glucose concentration. The GAF-CNN model can be used to establish a calibration model to predict blood glucose concentration.

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.

Raman spectroscopy and sciatic functional index (SFI) after low-level laser therapy (LLLT) in a rat sciatic nerve crush injury model

Axonotmesis causes sensorimotor and neurofunctional deficits, and its regeneration can occur slowly or not occur if not treated appropriately. Low-level laser therapy (LLLT) promotes nerve regeneration with the proliferation of myelinating Schwann cells to recover the myelin sheath and the production of glycoproteins for endoneurium reconstruction. This study aimed to evaluate the effects of LLLT on sciatic nerve regeneration after compression injury by means of the sciatic functional index (SFI) and Raman spectroscopy (RS). For this, 64 Wistar rats were divided into two groups according to the length of treatment: 14 days (n = 32) and 21 days (n = 32). These two groups were subdivided into four sub-groups of eight animals each (control 1; control 2; laser 660 nm; laser 808 nm). All animals had surgical exposure to the sciatic nerve, and only control 1 did not suffer nerve damage. To cause the lesion in the sciatic nerve, compression was applied with a Kelly clamp for 6 s. The evaluation of sensory deficit was performed by the painful exteroceptive sensitivity (PES) and neuromotor tests by the SFI. Laser 660 nm and laser 808 nm sub-groups were irradiated daily (100 mW, 40 s, energy density of 133 J/cm²). The sciatic nerve segment was removed for RS analysis. The animals showed accentuated sensory and neurofunctional deficit after injury and their rehabilitation occurred more effectively in the sub-groups treated with 660 nm laser. Control 2 sub-group did not obtain functional recovery of gait. The RS identified sphingolipids (718, 1065, and 1440 cm^-1) and collagen (700, 852, 1004, 1270, and 1660 cm^-1) as biomolecular characteristics of sciatic nerves. Principal component analysis revealed important differences among sub-groups and a directly proportional correlation with SFI, mainly in the sub-group laser 660 nm treated for 21 days. In the axonotmesis-type lesion model presented herein, the 660 nm laser was more efficient in neurofunctional recovery, and the Raman spectra of lipid and protein properties were attributed to the basic biochemical composition of the sciatic nerve.

Use of Raman Spectroscopy, Scanning Electron Microscopy and Energy Dispersive X-ray Spectroscopy in a Multi-Technique Approach for Physical Characterization of Purple Urine Bag Syndrome

Purple urine bag syndrome (PUBS) is a rare condition characterized by purple discoloration of urine and urine bags. Although it is benign, it represents an alarming symptom to the patients and their relatives because of purple discoloration. We have physically characterized urine and urine bags belonging to a patient suffering from PUBS using an approach that combines Raman spectroscopy (RS) and scanning electron microscopy (SEM) coupled with energy dispersive X-ray (EDX). Five “blue” discolored bags and one sterile urine bag, representing the control, were cut into 1 cm2 square samples and analyzed by using RS and SEM + EDX technique. RS enabled us to identify the presence of indigo, a metabolite of tryptophan, while SEM analysis showed the biofilm deposit, probably due to the presence of microorganisms, and the EDX measurements exhibited the elemental composition of the bags. In particular, urine bags before and after the presence of PUBS urine showed an increase of ~32% of Cl, ~33% of O, ~667% of Ca, ~65% of Al and Mg, while C decreased by about 41%. Our results, to be taken as a proof-of-principle study, are promising for the aim to characterizing the urine bags in a flexible, inexpensive, and comprehensive manner.

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.

Quantifying Serum Total Lipids and Tryptophan Concentrations by Raman Spectroscopy During Standardized Obstacle Course in Horses

Raman spectroscopy is an inelastic light-scattering phenomenon that provides vibrational spectrum that contains information relative to chemical bonds and symmetry of a specific molecule, allowing the quali-quantitative simultaneous determination of several components in the biological fluids. Raman spectroscopy measurement returns a spectrum over a wavenumber range constituted by several bands representing biomarkers according to investigated biological matrices. In literature, it has been reported that at the frequencies inside the (1,300-1,360) cm^-1 total lipids, and tryptophan have been identified. On the basis of that, the aim of the present study was to compare the serum concentration of total lipids and tryptophan in horses subjected to a standardized obstacle course, in comparison with the data obtained in the (1,300-1,360) cm^-1 band. At this purpose, five clinically healthy and regularly trained Italian Saddle horses aged between 8, and 10 years old performed with the same rider an obstacle course of 350 m/min with twelve 1.30 m high jumps preceded by warm-up. Blood samples were collected by means of jugular venipuncture into a vacutainer tubes with clot activator at rest, after the exercise, and 30 minutes after the end of exercise. A high correlation was observed between the area of total lipids and tryptophan in the (1,300-1,360) cm^-1 band and their serum concentrations in all experimental conditions. Our preliminary results give a hint to study the exact correspondence between the area that identify these parameters in Raman spectrum and their serum concentration in athletic horses.

Simultaneous detection of glucose, triglycerides, and total cholesterol in whole blood by Fourier-Transform Raman spectroscopy

In this paper, a reagent-free simultaneous and direct detection method of three analytes in human blood based on Fourier-transform Raman (FT-Raman) spectroscopy with 1064 nm laser radiation was proposed for the first time. A total of 161 human blood samples were characterized by FT-Raman spectroscopy under the excitation laser source of 1064 nm. In order to achieve a robust regression model, the Nonlinear Iterative Partial Least Squares (NIPALS) with orthogonal signal correction (OSC) algorithm and sample set partition based on a joint x-y distance (SPXY) is used to establish multivariate calibration models. The root means square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP), correlation coefficients (R²) and ratio of performance to deviation (RPD) were 0.34255 mg/dL, 0.3662 mg/dL, 0.99982 and 56.3524 for glucose, 0.33656 mg/dL, 0.75736 mg/dL, 0.99967 and 34.9169 for total cholesterol (TC), and 0.29956 mg/dL, 0.27469 mg/dL, 0.99998 and 173.5098 for triglycerides (TG), respectively. The analysis results showed that the proposed method could be able to accurately predict the concentration of glucose, TC and TG in blood. This method can instantaneous multi-component detection on whole blood.

The interaction of chondroitin sulfate with a lipid monolayer observed by using nonlinear vibrational spectroscopy

The first vibrational sum-frequency generation (VSFG) spectra of chondroitin sulfate (CS) interacting with dipalmitoyl phosphatidylcholine (DPPC) at air–liquid interface are reported here, collected at a laser repetition rate of 100 kHz. By studying the VSFG spectra in the regions of 1050–1450 cm⁻¹, 2750–3180 cm⁻¹, and 3200–3825 cm⁻¹, it was concluded that in the presence of Ca²⁺ ions, the head groups together with the head-group-bound water molecules in the DPPC monolayer are strongly influenced by the interaction with CS, while the organization of the phospholipid tails remains mostly unchanged. The interactions were observed at a CS concentration below 200 nM, which exemplifies the potential of VSFG in studying biomolecular interactions at low physiological concentrations. The VSFG spectra recorded in the O–H stretching region at chiral polarization combination imply that CS molecules are organized into ordered macromolecular superstructures with a chiral secondary structure.

Chondroitin sulfate interacts with the headgroups of a lipid monolayer at the air–liquid interface and shows a chiral secondary structure.

Comparison of Surface-Enhanced Raman Scattering Properties of Serum and Urine for the Detection of Chronic Kidney Disease in Patients

Chronic kidney disease (CKD) affects more than 10% of the global population and is associated with significant morbidity and mortality. In most cases, this disease is developed silently, and it can progress to the end-stage renal failure. Therefore, early detection becomes critical for initiating effective interventions. Routine diagnosis of CKD requires both blood test and urinalyses in a clinical laboratory, which are time-consuming and have low sensitivity and specificity. Surface-enhanced Raman scattering (SERS) is an emerging method for rapidly assessing kidney function or injury. This study was designed to compare the differences between the SERS properties of the serum and urine for easy and simple detection of CKD. Enrolled for this study were 126 CKD patients (Stages 2-5) and 97 healthy individuals. SERS spectra of both the serum and urine samples were acquired using a Raman spectrometer (785 nm excitation). The correlation of chemical parameters of kidney function with the spectra was examined using prinicpal component analysis (PCA) combined with linear discriminant analysis (LDA) and partial least squares (PLS) analysis. Here, we showed that CKD was discriminated from non-CKD controls using PCA-LDA with a sensitivity of 74.6% and a specificity of 93.8% for the serum spectra, and 78.0% and 86.0 % for the urine spectra. The integration area under the receiver operating characteristic curve was 0.937 ± 0.015 (p < 0.0001) for the serum and 0.886 ± 0.025 (p < 0.0001) for the urine. The different stages of CKD were separated with the accuracy of 78.0% and 75.4% by the serum and urine spectra, respectively. PLS prediction (R²) of the serum spectra was 0.8540 for the serum urea (p < 0.001), 0.8536 for the serum creatinine (p < 0.001), 0.7500 for the estimated glomerular filtration rate (eGFR) (p < 0.001), whereas the prediction (R²) of urine spectra was 0.7335 for the urine urea (p < 0.001), 0.7901 for the urine creatinine (p < 0.001), 0.4644 for the eGFR (p < 0.001) and 0.6579 for the urine microalbumin (p < 0.001). In conclusion, the accuracy of associations between SERS findings of the serum and urine samples with clinical conclusions of CKD diagnosis in this limited number of patients is similar, suggesting that SERS may be used as a rapid and easy-to-use method for early screening of CKD, which however needs further evaluation in a large cohort study.

Analytical performance of Raman spectroscopy in assaying biochemical components in human serum

Chronic non-infectious diseases are important to research as they are the main causes of death in Brazil and worldwide. One very important chronic non-infectious disease is cardiovascular disease, whose risk factors (diabetes, dyslipidemia, and renal failure) can be detected through assessments of serum biochemical components. The objective of this study was to evaluate the analytical performance of Raman spectroscopy for analysis of lipid profile (total cholesterol, triglycerides, and HDL cholesterol), non-protein nitrogenous compounds (urea and creatinine), and glucose in 242 human serum samples. Models to discriminate and quantify the samples were developed using the predicted concentration by quantitative regression model based on partial least squares (PLS). The analytical error for the "leave-one-out" cross-validation based on the predicted PLS concentration was 10.5 mg/dL for total cholesterol, 21.4 mg/dL for triglyceride, 13.0 mg/dL for HDL cholesterol, 4.9 mg/dL for urea, 0.21 mg/dL for creatinine, and 15.4 mg/dL for glucose. The Kappa coefficient indicate very good agreement for cholesterol (0.83), good for triglyceride (0.77), urea (0.70) and creatinine (0.66), and fair for HDL cholesterol (0.38) and glucose (0.30). The results of the analytical performance demonstrated that Raman spectroscopy can be considered an important methodology to screen the population, especially for serum triglycerides and cholesterol.

Application of Raman Spectroscopy for the Evaluation of Metabolomic Dynamic Analysis in Athletic Horses

Raman spectroscopy is a rapid qualitative and quantitative technique that allows the simultaneous determination of several components, both biomolecules both chemical compounds, in the biological fluids to assess the metabolic status. In this study, the serum composition was evaluated in regularly trained athletic horses using Raman spectroscopy to identify biomarkers of sports performance. Five clinically healthy and regularly trained Italian Saddle horses were subjected to a standardized obstacle course (350 m/minute; eleven 1.25 high jumps) preceded by a warm-up. On the collected sera, at rest, immediately after exercise, 30 minutes, and 1 hour after the end of the exercise Raman measurements were performed using a diode laser with the excitation wavelength of 785 nm. The analysis of the obtained spectra allowed the identification of peaks and bands different in position and intensity among the experimental conditions. The acquired spectra, obtained from horse sera collected during the experimental protocol, were visually similar, except for the large band detected in the 1,250-1,800 cm^-1 range. The spectral intensity of the Raman spectrum decreased after training and 30 minutes after the end of exercise respect to the before exercise value, to come to the basal value after 60 minutes the end of the exercise. In conclusion, we can claim the ability of Raman spectroscopy to reveal the metabolic status of horses after physical exercise.

Normal-subtracted preprocessing of Raman spectra aiming to discriminate skin actinic keratosis and neoplasias from benign lesions and normal skin tissues

The differences in the biochemistry of normal and cancerous tissue could be better exploited by Raman spectroscopy when the spectral information from normal tissue is subtracted from the abnormal tissues. In this study, we evaluated the use of the normal-subtracted spectra to evidence the biochemical differences in the pre-cancerous and cancerous skin tissues compared with normal skin, and to discriminate the groups with altered tissues with respect to the normal sites. Raman spectra from skin tissues [normal (Normal), benign (dermatitis-BEN), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and actinic keratosis (KER)] were obtained in vivo (Silveira et al., 2015, doi: https://doi.org/10.1002/lsm.22318) and used to develop the spectral model. The mean spectrum of the normal sites (circumjacent to each lesion) from each subject was calculated and subtracted from each individual spectrum of that particular subject independently of the group (Normal, BEN, BCC, SCC, KERAT). The mean spectra of each altered group and the mean spectra of the differences were firstly evaluated in terms of biochemical contribution or differentiation comparing the normal site. Then, the normal-subtracted spectra were submitted to discriminant models based on partial least squares and principal components regression (PLS-DA and PCR-DA), and the discrimination were compared with the model using non-subtracted spectra. Results showed that the peaks of nucleic acids, lipids (triolein) and proteins (elastin and collagens I, III, and IV) were significantly different in the lesions, higher for the pre- and neoplastic lesions compared with normal and benign. The PLS-DA showed that the groups could be discriminated with 90.3% accuracy when the mean-subtracted spectra were used, contrasting with 75.1% accuracy when the non-subtracted spectra were used. Also, when discriminating non-neoplastic tissue (Normal + BEN) from pre- and neoplastic sites (BCC + SCC + KERAT), the accuracy increases to 92.5% for the normal-subtracted compared with 85.3% for the non-subtracted. The subtraction of the mean normal spectrum from the subject obtained circumjacent to each lesion could significantly increase the diagnostic capability of the Raman-based discrimination algorithm.

Diagnosing sickle cell disease and iron deficiency anemia in human blood by Raman spectroscopy

This work proposed the diagnosis of iron deficiency anemia (IDA) and sickle cell disease (SCD) in human blood caused by iron deficiency and hemoglobin S (HbS), which are among the most common anemias, by means of Raman spectroscopy. Whole blood samples from patients diagnosed with IDA and HbS, as well as from normal subjects (HbA), were obtained and submitted to Raman spectroscopy (830 nm, 150 mW, 400-1800 cm^-1 spectral range, 4 cm^-1 resolution). Difference spectra of IDA-HbA showed spectral features of hemoglobin with less intensity in the IDA, whereas the difference spectra of SCD-HbA showed spectral features of deoxyhemoglobin increased and of oxyhemoglobin decreased in SCD. An exploratory analysis by principal components analysis (PCA) showed that the peaks referred to oxy- and deoxyhemoglobin markedly differentiated SCD and HbA, as well as the increased amount of hemoglobin features in the SCD group, suggesting increased erythropoiesis. The IDA group showed hemoglobin features with lower intensities as well as peaks referred to the iron bonding to the porphyrin ring with reduced intensities when compared to the HbA. Discriminant analysis based on partial least squares (PLS-DA) and PCA (PCA-DA) showed that the IDA and SCD anemias could be discriminated from the HbA spectra with 95.0% and 93.8% of accuracy, for the PLS and PCA respectively, with sensitivity/specificity of 93.8%/95.7% for the PLS-DA model. The iron depletion and the sickling of erythrocytes could be identified by Raman spectroscopy and a spectral model based on PLS accurately discriminated these IDA and SCD samples from the normal HbA.

Vibrational Spectroscopy Fingerprinting in Medicine: from Molecular to Clinical Practice

In the last two decades, Fourier Transform Infrared (FTIR) and Raman spectroscopies turn out to be valuable tools, capable of providing fingerprint-type information on the composition and structural conformation of specific molecular species. Vibrational spectroscopy’s multiple features, namely highly sensitive to changes at the molecular level, noninvasive, nondestructive, reagent-free, and waste-free analysis, illustrate the potential in biomedical field. In light of this, the current work features recent data and major trends in spectroscopic analyses going from in vivo measurements up to ex vivo extracted and processed materials. The ability to offer insights into the structural variations underpinning pathogenesis of diseases could provide a platform for disease diagnosis and therapy effectiveness evaluation as a future standard clinical tool.

Investigation of chemical composition of meat using spatially off-set Raman spectroscopy

Spatially off-set Raman spectroscopy (SORS) offers non-invasive chemical characterisation of the sub-surface of various biological tissues as it permits the assessment of diffusely scattering samples at depths of several orders of magnitude deeper than conventional Raman spectroscopy. Chemicals such as glycogen, glucose, lactate and cortisol are predictors of meat quality, however detection of these chemicals is limited to the surface of meat using conventional Raman spectroscopy as their concentration is higher within the tissue. Here, we have used SORS to detect spectral bands for glycogen, lactate, glucose and cortisol beneath the surface of meat tissue by spiking. To our knowledge, this is the first report on this method for potential application in meat quality analysis. We further validate our SORS spectral results using chemometric analysis to determine chemical-specific spectral characteristics suitable for chemical identification. The chemometric analysis clearly shows distinction of spiked metabolites into four distinct groups, even for such chemically similar compounds as glucose, glycogen and lactate.

Biochemical changes in injured sciatic nerve of rats after low-level laser therapy (660 nm and 808 nm) evaluated by Raman spectroscopy

The aim of this study was to identify biochemical changes in sciatic nerve (SN) after crush injury and low-level laser therapy (LLLT) with 660 nm and 808 nm by Raman spectroscopy (RS) analysis. A number of 32 Wistar rats were used, divided into four groups (control 1, control 2, LASER 660 nm, and LASER 808 nm). All animals underwent surgical procedure of the SN and groups control 2, LASER 660 nm, and LASER 808 nm were submitted to SN crush damage (axonotmesis). The LLLT in the groups LASER 660 nm and LASER 808 nm was applied daily for 21 consecutive days (100 mW, 30 s, 133 J/cm² fluence). The hind paw was removed and the SN was dissected and positioned on an aluminum support to collect dispersive Raman spectra (830 nm excitation, 30 s accumulation). To estimate the biochemical changes in the SN associated with LLLT, the principal component analysis (PCA) was applied. The Raman spectra of the sciatic nerve fragments showed peaks of the major biochemical components of the nerve, especially sphingolipids, phospholipids, glycoproteins, and collagen. The spectral features identified in some of the principal component loading vectors are referred to the biochemical elements present on the SN and were increased in the groups treated with LLLT, mainly lipids (sphingo and phospholipids) and proteins (collagen)-constituents of the myelin sheath. The RS was effective in identifying the biochemical differences in the SN after the crush injury, and LASER 660 nm was more efficient than the LASER 808 nm in cell proliferation and repair of the injured SN.

A high-throughput serum Raman spectroscopy platform and methodology for colorectal cancer diagnostics

Translating Raman spectroscopy for colorectal cancer diagnosis with a focus on high-throughput design, inter-user variability and sample handling considerations.

Application of Raman spectroscopy in type 2 diabetes screening in blood using leucine and isoleucine amino-acids as biomarkers and in comparative anti-diabetic drugs efficacy studies

Diabetes is an irreversible condition characterized by elevated blood glucose levels. Currently, there are no predictive biomarkers for this disease and the existing ones such as hemoglobin A1c and fasting blood glucose are used only when diabetes symptoms are noticed. The objective of this work was first to explore the potential of leucine and isoleucine amino acids as diabetes type 2 biomarkers using their Raman spectroscopic signatures. Secondly, we wanted to explore whether Raman spectroscopy can be applied in comparative efficacy studies between commercially available anti-diabetic drug pioglitazone and the locally used anti-diabetic herbal extract Momordica spinosa (Gilg.)Chiov. Sprague Dawley (SD) rat’s blood was used and were pipetted onto Raman substrates prepared from conductive silver paste smeared glass slides. Prominent Raman bands associated with glucose (926, 1302, 1125 cm⁻¹), leucine (1106, 1248, 1302, 1395 cm⁻¹) and isolecucine (1108, 1248, 1437 and 1585 cm⁻¹) were observed. The Raman bands centered at 1125 cm⁻¹, 1395 cm⁻¹ and 1437 cm⁻¹ associated respectively to glucose, leucine and isoleucine were chosen as biomarker Raman peaks for diabetes type 2. These Raman bands displayed decreased intensities in blood from diabetic SD rats administered antidiabetic drugs pioglitazone and herbal extract Momordica spinosa (Gilg.)Chiov. The intensity decrease indicated reduced concentration levels of the respective biomarker molecules: glucose (1125 cm⁻¹), leucine (1395 cm⁻¹) and isoleucine (1437 cm⁻¹) in blood. The results displayed the power and potential of Raman spectroscopy in rapid (10 seconds) diabetes and pre-diabetes screening in blood (human or rat’s) with not only glucose acting as a biomarker but also leucine and isoleucine amino-acids where intensities of respectively assigned bands act as references. It also showed that using Raman spectroscopic signatures of the chosen biomarkers, the method can be an alternative for performing comparative efficacy studies between known and new anti-diabetic drugs. Reports on use of Raman spectroscopy in type 2 diabetes mellitus screening with Raman bands associated with leucine and isoleucine molecules acting as reference is rare in literature. The use of Raman spectroscopy in pre-diabetes screening of blood for changes in levels of leucine and isoleucine amino acids is particularly interesting as once elevated levels are noticed, necessary interventions to prevent diabetes development can be initiated.