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%).

Diagnosing COVID-19 in nasopharyngeal secretion through Raman spectroscopy: a feasibility study.

Since the beginning of the COVID-19 pandemic, the scientific community has sought to develop fast and accurate techniques for detecting the SARS-CoV-2 virus. Raman spectroscopy is a promising technique for diagnosing COVID-19 through serum samples. In the present study, the diagnosis of COVID-19 through nasopharyngeal secretion has been proposed. Raman spectra from nasopharyngeal secretion samples (15 Control, negative and 12 COVID-19, positive, assayed by immunofluorescence antigen test) were obtained in triplicate in a dispersive Raman spectrometer (830 nm, 350 mW), accounting for a total of 80 spectra. Using principal component analysis (PCA) the main spectral differences between the Control and COVID-19 samples were attributed to N and S proteins from the virus in the COVID-19 group. Features assigned to mucin (serine, threonine and proline amino acids) were observed in the Control group. A binary model based on partial least squares discriminant analysis (PLS-DA) differentiated COVID-19 versus Control samples with accuracy of 91%, sensitivity of 80% and specificity of 100%. Raman spectroscopy has a great potential for becoming a technique of choice for rapid and label-free evaluation of nasopharyngeal secretion for COVID-19 diagnosis.

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.

Biochemical Analysis of Urine Samples from Diabetic and Hypertensive Patients without Renal Dysfunction Using Spectrophotometry and Raman Spectroscopy Techniques Aiming Classification and Diagnosis.

The purpose of this study was to perform a comparative biochemical analysis between conventional spectrophotometry and Raman spectroscopy, techniques used for diagnoses, on the urine of healthy (CT) and diabetic and hypertensive patients (DM&HBP). Urine from 40 subjects (20 in the CT group and 20 in the DM&HBP group) was examined in a dispersive Raman spectrometer (an 830 nm excitation and a 350 mW power). The mean Raman spectra between both groups showed a significant difference in peaks of glucose; exploratory analysis by principal component analysis (PCA) identified spectral differences between the groups, with higher peaks of glucose and proteins in the DM&HBP group. A partial least squares (PLS) regression model estimated by the Raman data indicated the concentrations of urea, creatinine, glucose, phosphate, and total protein; creatinine and glucose were the biomarkers that presented the best correlation coefficient (r) between the two techniques analyzed (r = 0.68 and r = 0.98, respectively), both with eight latent variables (LVs) and a root mean square error of cross-validation (RMSecv) of 3.6 and 5.1 mmol/L (41 and 92 mg/dL), respectively. Discriminant analysis (PLS-DA) using the entire Raman spectra was able to differentiate the samples of the groups in the study, with a higher accuracy (81.5%) compared to the linear discriminant analysis (LDA) models using the concentration values of the spectrometric analysis (60.0%) and the concentrations predicted by the PLS regression (69.8%). Results indicated that spectral models based on PLS applied to Raman spectra may be used to distinguish subjects with diabetes and blood hypertension from healthy ones in urinalysis aimed at population screening.

Diagnosing molecular subtypes of breast cancer by means of Raman spectroscopy.

OBJECTIVES: Raman spectroscopy has been used to discriminate human breast cancer and its different tumor molecular subtypes (luminal A, luminal B, HER2, and triple-negative) from normal tissue in surgical specimens. MATERIALS AND METHODS: Breast cancer and normal tissue samples from 31 patients were obtained by surgical resection and submitted for histopathology. Before anatomopathological processing, the samples had been submitted to Raman spectroscopy (830 nm, 25 mW excitation laser parameters). In total, 424 Raman spectra were obtained. Principal component analysis (PCA) was used in an exploratory analysis to unveil the compositional differences between the tumors and normal tissues. Discriminant models were developed to distinguish the different cancer subtypes by means of partial least squares (PLS) regression. RESULTS: PCA vectors showed spectral features referred to the biochemical constitution of breast tissues, such as lipids, proteins, amino acids, and carotenoids, where lipids were decreased and proteins were increased in breast tumors. Despite the small spectral differences between the different subtypes of tumor and normal tissues, the discriminant model based on PLS was able to discriminate the spectra of the breast tumors from normal tissues with an accuracy of 97.3%, between luminal and nonluminal subtypes with an accuracy of 89.9%, between nontriple-negative and triple-negative with an accuracy of 94.7%, and each molecular subtype with an accuracy of 73.0%. CONCLUSION: PCA could reveal the compositional difference between tumors and normal tissues, and PLS could discriminate the Raman spectra of breast tissues regarding the molecular subtypes of cancer, being a useful tool for cancer diagnosis.

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.

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.

Use of Raman spectroscopy to evaluate the biochemical composition of normal and tumoral human brain tissues for diagnosis.

Raman spectroscopy was used to identify biochemical differences in normal brain tissue (cerebellum and meninges) compared to tumors (glioblastoma, medulloblastoma, schwannoma, and meningioma) through biochemical information obtained from the samples. A total of 263 spectra were obtained from fragments of the normal cerebellum (65), normal meninges (69), glioblastoma (28), schwannoma (8), medulloblastoma (19), and meningioma (74), which were collected using the dispersive Raman spectrometer (830 nm, near infrared, output power of 350 mW, 20 s exposure time to obtain the spectra), coupled to a Raman probe. A spectral model based on least squares fitting was developed to estimate the biochemical concentration of 16 biochemical compounds present in brain tissue, among those that most characterized brain tissue spectra, such as linolenic acid, triolein, cholesterol, sphingomyelin, phosphatidylcholine, ß-carotene, collagen, phenylalanine, DNA, glucose, and blood. From the biochemical information, the classification of the spectra in the normal and tumor groups was conducted according to the type of brain tumor and corresponding normal tissue. The classification used in discrimination models were (a) the concentrations of the biochemical constituents of the brain, through linear discriminant analysis (LDA), and (b) the tissue spectra, through the discrimination by partial least squares (PLS-DA) regression. The models obtained 93.3% discrimination accuracy through the LDA between the normal and tumor groups of the cerebellum separated according to the concentration of biochemical constituents and 94.1% in the discrimination by PLS-DA using the whole spectrum. The results obtained demonstrated that the Raman technique is a promising tool to differentiate concentrations of biochemical compounds present in brain tissues, both normal and tumor. The concentrations estimated by the biochemical model and all the information contained in the Raman spectra were both able to classify the pathological groups.

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.

Production and viscosity of Xanthan Gum are increased by LED irradiation of X. campestris cultivated in medium containing produced water of the oil industry.

Oil recovery is a challenge and microbial enhanced oil recovery is an option. We theorized that the use of produced water (PW) with photo-stimulation could influence both production and viscosity of Xanthan gum. This study aimed at the evaluation of the effect of photo-stimulation by λ630 ± 1 ηm LED light on the biosynthesis of Xanthan gum produced by Xanthomonas campestris IBSBF 2103 strain reusing PW of the oil industry. We assessed the effect of photo-stimulation by LED light (λ630 nm) on the biosynthesis of Xanthan gum produced by X. campestris in medium containing produced water. Different energy densities applied during the microbial growth phase were tested. The highest production was achieved when using 12 J/cm2 LED light (p < 0.01). Three protocols were assessed: Non-irradiated (Control), Irradiation with LED light during the growth phase (LEDgrowth) and Irradiation with LED light during both growth and production phases (LED growth+production). Both the amount and viscosity of the xanthan gum was significantly higher (p < 0.01) in the group LEDgrowth+production. The study showed that LED irradiation (λ630 ± 1 ηm) during both the growth and production phases of the biopolymer increased both the production and viscosity of Xanthan gum.

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.

Biochemical characterization of pathogenic bacterial species using Raman spectroscopy and discrimination model based on selected spectral features.

This study aimed to evaluate the differences in the Raman spectra of nine clinical species of bacteria isolated from infections (three Gram-positive and six Gram-negative species), correlating the spectra with the chemical composition of each species and to develop a classification model through discriminant analysis to categorize each bacterial strain using the peaks with the most significant differences. Bacteria were cultured in Mueller Hinton agar and a sample of biomass was harvested and placed in an aluminum sample holder. A total of 475 spectra from 115 different strains were obtained through a dispersive Raman spectrometer (830 nm) with exposure time of 50 s. The intensities of the peaks were evaluated by one-way analysis of variance (ANOVA) and the peaks with significant differences were related to the differences in the biochemical composition of the strains. Discriminant analysis based on quadratic distance applied to the peaks with the most significant differences and partial least squares applied to the whole spectrum showed 89.5% and 90.1% of global accuracy, respectively, for classification of the spectra in all the groups. Raman spectroscopy could be a promising technique to identify spectral differences related to the biochemical content of pathogenic microorganisms and to provide a faster diagnosis of infectious diseases.

Temperature-Induced Chemical Changes in Lubricant Automotive Oils Evaluated Using Raman Spectroscopy.

Automotive engine lubricating oils are not only intended to reduce friction between parts, but also act on the cooling of motor components and protection of metals against corrosion. To improve its properties and efficiency, additives are added to the base oil for different goals. However, over time of use, external factors modify its properties, such as the engine operating temperature, the frictional force between parts, the mixture of this oil with fuel before burning and with combustion products, causing loss of their efficiency. This work aimed to evaluate, with Raman spectroscopy technique, the temperature-induced changes related to degradation of mineral, semi-synthetic and synthetic automotive lubricating oils. Samples being subject to periodic heating cycle were kept to average temperature of 133 ℃, considering 8 h per day, for six days, until complete 48 h of heating. By analyzing the Raman spectra, it was possible to identify common peaks between the three types of oils and changes caused by heating cycles. Principal components analysis showed that the synthetic oil degraded in less extent than the semi-synthetic one, and this one degraded less than the mineral oil. Spectral models to predict the heating time based on the spectral variations identified using principal components analysis and the regression done using partial least squares, using the heating time as independent variable and the spectral features as dependent variables, was able to predict the heating time for each of oil types with high correlation and prediction error (r > 0.97 and error <4.0 h) for both principal components analysis and partial least squares regression models. Raman technique was able to identify chemical changes resulting from the heating of lubricant oils and to correlate these changes with the heating time, thus becoming a technique of interest for the preventive maintenance area.

Effects of photo-stimulation with laser or LED on the composition of Xanthan gum produced in media containing distilled water or dialyzed or not produced water by means of Raman spectroscopy.

Oil is expected to continue to be one of the most important sources of energy in the world and world's energy matrix for the foreseeable future. However, high demand for energy and the decline of the production of oil fields makes oil recovery a challenge. Most techniques used for the recovery process are expensive, non-sustainable and technically difficult to implement. In this context, microbial enhanced oil recovery (MEOR) represents an attractive alternative. It employs products derived from the metabolism of microorganisms that produce biopolymers. Certain bacteria species (e.g., Xanthomonas campestris) produce polysaccharides (exopolysaccharides - EPS) such as the well-known Xanthan gum (XG). We hypothesized that the use of produced water (PW) water in combination photo-stimulation with laser/LED could influence the production and composition of XG. Raman spectroscopy has been used for qualitative and quantitative evaluation of the biochemical composition of XG biopolymer under light stimulation. X. campestris cultures in either distilled water or dialysis-produced water were studied under the absence or presence of laser irradiation (λ = 660 nm, CW, spot size 0.040 cm2, 40 mW, 444 s, 8.0 J/cm2) or LED (λ = 630 nm ± 2 nm, CW, spot size 0.50 cm2, 140 mW, 500 s, 12 J/cm2). XG produced by these cultures was analyzed by Raman spectroscopy at 1064 nm excitation and subjected to principal component analysis (PCA). Results of the exploratory analysis and ANOVA general linear model (GLM) suggested that the extent of XG and pyruvate (pyruvyl mannose) production was affected differentially in X. campestris when cultured in distilled water plus LED photo-stimulation versus dialysis-produced water plus LED photo-stimulation. XG production increased in the distilled water culture. In contrast, both pyruvate acetyl mannose content went up in the dialysis-water culture. These results open a wide field of opportunities in the use of metal-enriched cultures in combination with photo-biomodulation to direct and optimize bacterial production of compounds (i.e., XG) that may be of great benefit in the implementation of sustainable practices for oil extraction.

Composition of Xanthan gum produced by Xanthomonas campestris using produced water from a carbonated oil field through Raman spectroscopy.

Produced water (PW) is a by-product generated throughout oil exploration. Geological formation and geographical location of the reservoir influence its physical, chemical and biological characteristics. Xanthan gum (XG), an exopolysaccharide (EPS) produced by Xanthomonas campestris, has been widely used in enhanced oil recovery (EOR) technology because of its high viscosity, pseudoplastic behavior, stability in function of salinity, temperature and alkaline conditions. The production of XG may be affected by the composition of the PW, where the acetyl and pyruvyl radicals may be present in the mannoses. The aim of this study was to evaluate the composition of XG produced by X. campestris, particularly the amount of Xanthan, acetyl and pyruvyl groups, in culture mediums containing distilled (DW) or produced (PW) water in different concentrations, by means of dispersive Raman spectroscopy (1064 nm). The spectra of XG showed peaks referred to the main constituents of the Xanthan (glucose, mannose and glucuronic acid). Spectral features assigned to pyruvyl were seen in all samples mainly at ~1010 cm-1, with higher intensity when using DW and 25% PW. PCA loadings showed that the peaks assigned to pyruvyl are consistent to presence of sodium pyruvate (~1040/~1050 and ~ 1432 cm-1) and were higher in the samples obtained in 25% PW. ANOVA GLM applied to Raman peaks of interest (~1010 and ~ 1090 cm-1) and to PCA scores (Score 1 to Score 3) showed that both were influenced by the type of water used in the culture medium, where the XG were strongly reduced in the groups PW compared to DW while the pyruvyl content increased proportionally with the concentration of PW. The results suggest that the composition of the water used in the bacteria's culture medium influenced the composition of XG, including the amount of Xanthan and particularly the pyruvyl content, and therefore needs to be considered when using this approach of injecting XG in oil fields as pyruvyl content affects viscosity.

Raman spectroscopic study of the effect of the use of laser/LED phototherapy on the repair of complete tibial fracture treated with internal rigid fixation.

This study aimed to assess the repair of complete surgical tibial fractures fixed with internal rigid fixation (IRF) associated or not to the use of mineral trioxide aggregate (MTA) cement and treated or not with laser (λ = 780 nm, infrared) or LED (λ = 850 ±â€¯10 nm, infrared) lights, 142.8 J/cm2 per treatment, by means of Raman spectroscopy. Open surgical tibial fractures were created on 18 rabbits (6 groups of 3 animals per group, ∼8 months old) and fractures were fixed with IRF. Three groups were grafted with MTA. The groups of IRF and IRF + MTA that received laser or LED were irradiated every other day during 15 days. Animals were sacrificed after 30 days, being the tibia surgically removed. Raman spectra were collected via the probe at the defect site in five points, resulting in 15 spectra per group (90 spectra in the dataset). Spectra were collected at the same day to avoid changes in laser power and experimental setup. The ANOVA general linear model showed that the laser irradiation of tibial bone fractures fixed with IRF and grafted with MTA had significant influence in the content of phosphate (peak ∼960 cm-1) and carbonated (peak ∼1,070 cm-1) hydroxyapatites as well as collagen (peak 1,452 cm-1). Also, peaks of calcium carbonate (1,088 cm-1) were found in the groups grafted with MTA. Based on the Raman spectroscopic data collected in this study, MTA has been shown to improve the repair of complete tibial fractures treated with IRF, with an evident increase of collagen matrix synthesis, and development of a scaffold of hydroxyapatite-like calcium carbonate with subsequent deposition of phosphate hydroxyapatite.

Detection of prostate cancer by Raman spectroscopy: A multivariate study on patients with normal and altered PSA values.

Digital rectal examination (DRE) was the primary means to detect prostate diseases. The DRE has a high variability as it is based manly in the tactile sensitivity and expertise of the examiner. The prostate-specific antigen (PSA) test was initially developed for surveillance of prostate cancer and later it was also used as a diagnosis test. Raman spectroscopy is a powerful analytical technique that can measure the chemical composition of complex biological samples, such as body fluids. Biochemical changes caused by diseases can lead to significant changes in the Raman spectra. This study aimed to identify the differences in the Raman spectra of serum samples with normal and altered PSA values and correlate these differences by using multivariate techniques (principal component analysis - PCA and partial least squares regression - PLS). A total of 321 spectra were collected from 108 subjects. Two hundred and seventy were obtained from 91 non-altered PSA samples and 51 spectra from 17 samples with altered PSA. Each spectrum acquired was standardized to the area under the curve (1-norm). Discriminating and quantitative models employing PCA and PLS were developed. The PCA analyses showed 85.7% predictive power (87.41% sensitivity and 76.47% specificity). The PLS test showed a near-perfect sensitivity (98.51%) and an intermediate specificity (62.75%). The quantitative model through PLS regression showed a good correlation between PSA values and the spectral features (r = 0.605). This preliminary study suggests that Raman spectroscopy could be efficiently used for screening patients with altered PSA as well as for follow-up of the treatment of the prostate cancer by using initially the PLS to identify the possible presence of the prostate cancer and later on use de PCA to confirm the diagnosis.

Detecting creatine excreted in the urine of swimming athletes by means of Raman spectroscopy.

High-level sport requires analysis of athletes' metabolic conditions in order to improve the training. Raman spectroscopy can be used to assess urinary composition advantageously when compared to conventional methods of urinalysis. In this work, Raman spectroscopy has been employed to detect creatine in urine of professional swimmers before and after training compared to sedentaries. It has been collected urine samples from five swimmers before and immediately after 150 min of swimming and submitted to Raman spectroscopy (830 nm excitation, 350 mW laser power, 20 s integration time) and compared to the urine from a control group (14 sedentary subjects). The Raman spectra of urine from four swimmers after training showed peaks related to creatine at 829, 915, 1049, and 1397 cm-1, besides peaks referred to urea, creatinine, ketone bodies, and phosphate. A spectral model estimated the concentration of creatine to be from 0.26 to 0.72 g/dL in the urine of these athletes. The presence of this metabolic biomarker in the urine of some swimmers suggests a metabolic profile influenced by the diet, supplementation, individual metabolism, and the self-response to the training. Raman spectroscopy allows a rapid and reliable detection of creatine excreted in the urine of swimming athletes, which may be used to adjust the nutrition/supplementation of each individual as well as the individual response and energy consumption depending on the type and duration of the training.

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.