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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Laser/LED phototherapy on the repair of tibial fracture treated with wire osteosynthesis evaluated by Raman spectroscopy.

The aim of the present study was to assess, by means of Raman spectroscopy, the repair of complete surgical tibial fractures fixed with wire osteosynthesis (WO) treated or not with infrared laser (λ780 nm) or infrared light emitting diode (LED) (λ850 ± 10 nm) lights, 142.8 J/cm2 per treatment, associated or not to the use of mineral trioxide aggregate (MTA) cement. Surgical tibial fractures were created on 18 rabbits, and all fractures were fixed with WO and some groups were grafted with MTA. Irradiated groups received lights at every other day during 15 days, and all animals were sacrificed after 30 days, being the tibia removed. The results showed that only irradiation with either laser or LED influenced the peaks of phosphate hydroxyapatite (~ 960 cm-1). Collagen (~ 1450 cm-1) and carbonated hydroxyapatite (~ 1070 cm-1) peaks were influenced by both the use of MTA and the irradiation with either laser or LED. It is concluded that the use of either laser or LED phototherapy associated to MTA cement was efficacious on improving the repair of complete tibial fractures treated with wire osteosynthesis by increasing the synthesis of collagen matrix and creating a scaffold of calcium carbonate (carbonated hydroxyapatite-like) and the subsequent deposition of phosphate hydroxyapatite.

Discrimination model applied to urinalysis of patients with diabetes and hypertension aiming at diagnosis of chronic kidney disease by Raman spectroscopy.

Higher blood pressure level and poor glycemic control in diabetic patients are considered progression factors that cause faster decline in kidney functions leading to kidney damage. The present study aimed to develop a quantification model of biomarkers creatinine, urea, and glucose by means of selected peaks of these compounds, measured by Raman spectroscopy, and to estimate the concentration of these analytes in the urine of normal subjects (G_N), diabetic patients with hypertension (G_WOL) patients with chronic renal failure doing dialysis (G_D). Raman peak intensities at 680 cm-1 (creatinine), 1004 cm-1 (urea), and 1128 cm-1 (glucose) from normal, diabetic, and hypertensive and doing dialysis patients, obtained with a dispersive 830 nm Raman spectrometer, were estimated through Origin software. Spectra of creatinine, urea, and glucose diluted in water were also obtained, and the same peaks were evaluated. A discrimination model based on Mahalanobis distance was developed. It was possible to determine the concentration of creatinine, urea, and glucose by means of the Raman peaks of the selected biomarkers in the urine of the groups G_N, G_WOL, and G_D (r = 0.9). It was shown that the groups G_WOL and G_D had lower creatinine and urea concentrations than the group G_N (p < 0.05). The classification model based on Mahalanobis distance applied to the concentrations of creatinine, urea, and glucose presented a correct classification of 89% for G_N, 86% for G_WOL, and 79% for G_D. It was possible to obtain quantitative information regarding important biomarkers in urine for the assessment of renal impairment in patients with diabetes and hypertension, and this information can be correlated with clinical criteria for the diagnosis of chronic kidney disease.

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

Raman spectroscopy has been employed in the quantitative analysis of biochemical components in human serum. This study aimed to develop a spectral model to estimate the concentration of glucose and lipid fractions in human serum, thus evaluating the feasibility of Raman spectroscopy technique for diagnostic purposes. A total of 44 samples of blood serum were collected from volunteers submitted to routine blood biochemical assay analysis. The biochemical concentrations of glucose, triglycerides, cholesterol, and high-density and low-density lipoproteins (HDL and LDL) were obtained by colorimetric method. Serum samples (200 µL) were submitted to Raman spectroscopy (830 nm, 250 mW, 50-s accumulation). The spectra of sera present peaks related to the main constituents, particularly proteins and lipids. A quantitative model based on partial least squares (PLS) regression has been developed to estimate the concentration of these compounds, taking the biochemical concentrations assayed by the colorimetric method as sample's actual concentrations. The PLS model based on leave-one-out cross-validation approach estimated the concentration of triglycerides and cholesterol with r = 0.98 and 0.96, and root mean square error of 35.4 and 15.9 mg/dL, respectively. For the other biochemicals, the r was ranging from 0.75 to 0.86. These results evidenced the possibility of performing biochemical assay in blood serum samples by Raman spectroscopy and PLS regression and may be employed as a means of diagnosis in routine clinical analysis.

Biochemical changes on the repair of surgical bone defects grafted with biphasic synthetic micro-granular HA + ß-tricalcium phosphate induced by laser and LED phototherapies and assessed by Raman spectroscopy.

This work aimed the assessment of biochemical changes induced by laser or LED irradiation during mineralization of a bone defect in an animal model using a spectral model based on Raman spectroscopy. Six groups were studied: clot, laser (λ = 780 nm; 70 mW), LED (λ = 850 ± 10 nm; 150 mW), biomaterial (biphasic synthetic micro-granular hydroxyapatite (HA) + ß-tricalcium phosphate), biomaterial + laser, and biomaterial + LED. When indicated, defects were further irradiated at a 48-h interval during 2 weeks (20 J/cm2 per session). At the 15th and 30th days, femurs were dissected and spectra of the defects were collected. Raman spectra were submitted to a model to estimate the relative amount of collagen, phosphate HA, and carbonate HA by using the spectra of pure collagen and biomaterials composed of phosphate and carbonate HA, respectively. The use of the biomaterial associated to phototherapy did not change the collagen formation at both 15 and 30 days. The amount of carbonate HA was not different in all groups at the 15th day. However, at the 30th day, there was a significant difference (ANOVA, p = 0.01), with lower carbonate HA for the group biomaterial + LED compared to biomaterial (p < 0.05). The phosphate HA was higher in the groups that received biomaterial grafts at the 15th day compared to clot (significant for the biomaterial; p < 0.01). At the 30th day, the phosphate HA was higher for the group biomaterial + laser, while this was lower for all the other groups. These results indicated that the use of laser phototherapy improved the repair of bone defects grafted with the biomaterial by increasing the deposition of phosphate HA.

Estimating the concentration of urea and creatinine in the human serum of normal and dialysis patients through Raman spectroscopy.

Urea and creatinine are commonly used as biomarkers of renal function. Abnormal concentrations of these biomarkers are indicative of pathological processes such as renal failure. This study aimed to develop a model based on Raman spectroscopy to estimate the concentration values of urea and creatinine in human serum. Blood sera from 55 clinically normal subjects and 47 patients with chronic kidney disease undergoing dialysis were collected, and concentrations of urea and creatinine were determined by spectrophotometric methods. A Raman spectrum was obtained with a high-resolution dispersive Raman spectrometer (830 nm). A spectral model was developed based on partial least squares (PLS), where the concentrations of urea and creatinine were correlated with the Raman features. Principal components analysis (PCA) was used to discriminate dialysis patients from normal subjects. The PLS model showed r = 0.97 and r = 0.93 for urea and creatinine, respectively. The root mean square errors of cross-validation (RMSECV) for the model were 17.6 and 1.94 mg/dL, respectively. PCA showed high discrimination between dialysis and normality (95 % accuracy). The Raman technique was able to determine the concentrations with low error and to discriminate dialysis from normal subjects, consistent with a rapid and low-cost test.

Analysis of experimental tendinitis in rats treated with laser and platelet-rich plasma therapies by Raman spectroscopy and histometry.

The objective of this controlled experimental study was to analyze the changes in the Achilles tendons of rats with experimentally induced tendinitis after treatment with platelet-rich plasma (PRP) and/or laser therapies by histometry to quantify fibroblasts and by Raman spectroscopy to determine the biochemical concentration of collagen types I and III. Fifty-four male Wistar rats were divided into six treatment groups: control (G1); PRP only (G2); irradiation with 660 nm laser (G3); irradiation with 830 nm laser (G4); PRP plus 660 nm laser irradiation (G5); and PRP plus 830 nm laser irradiation (G6). Injuries (partial tenotomy) were inflicted in the middle third of the Achilles tendon, with PRP added prior to suture in the appropriate experimental groups. A diode laser (model Laser Flash® III, DMC Equipamentos Ltda, São Carlos, SP, Brazil) that can be operated in two wavelengths 660 and 830 nm was used for irradiation treatments. The irradiation protocol was energy density of 70 J/cm², 20 s irradiation time, and 0.028 cm² spot area, per point in three points in the injured. The histometry was made in micrographical images of the H&E stained sections and evaluated by ImageJ (version 1.46r)®. Raman spectra were collected using a dispersive spectrometer at 830 nm excitation, 200 mW power, and 10 s integration time (P-1 Raman system, Lambda Solutions, Inc. MA, USA). The relative amount of type I collagen was significantly greater in the PRP plus 830 nm laser irradiation group (468 ± 188) than in the control (147 ± 137), 630 nm laser only (191 ± 117), and 830 nm laser only (196 ± 106) groups (p < 0.01), while the quantity of type III collagen was significantly greater in the PRP-only group compared to both irradiated groups without PRP (p < 0.05). Treatment with PRP combined with irradiation at 830 nm resulted in a larger number of fibroblasts and increased concentration of type I collagen, thus accelerating the healing of the injured tendon.

Discrimination of non-melanoma skin lesions from non-tumor human skin tissues in vivo using Raman spectroscopy and multivariate statistics.

BACKGROUND AND OBJECTIVE: Raman spectroscopy was used to discriminate human non-melanoma skin lesions from non-tumor tissues in vivo. This work proposed the discrimination between non-melanoma (basal cell carcinoma, BCC; squamous cell carcinoma, SCC) and pre-cancerous lesions (actinic keratosis, AK) from benign lesions and normal (non-tumor group, NT) tissues, using near-infrared Raman spectroscopy with a Raman probe. MATERIALS AND METHODS: Prior to surgery, the spectra of suspicious lesions were obtained in situ. The spectra of adjacent, clinically normal skin were also obtained. Lesions were resectioned and submitted for histopathology. The Raman spectra were measured using a Raman spectrometer (830 nm). Two types of discrimination models were developed to distinguish the different histopathological groups. The principal components analysis discriminant analysis (PCA/DA) and the partial least squares discriminant analysis (PLS/DA) were based on Euclidean, quadratic and Mahalanobis distances. RESULTS: PCA and PLS spectral vectors showed spectral features of skin constituents, such as lipids (between 1,250 cm(-1) and 1,300 cm(-1) and at 1,450 cm(-1)) and proteins (between 870 cm(-1) and 940 cm(-1), 1,240 cm(-1) and 1,271 cm(-1), and at 1,000 cm(-1) and 1,450 cm(-1)). Despite the small spectral differences between malignant lesions and benign tissues, the algorithms discriminated the spectra of non-melanoma skin and pre-cancerous lesions from benign and normal tissues, with an overall accuracy of 82.8% and 91.9%, respectively. CONCLUSION: PCA and PLS could discriminate Raman spectra of skin tissues, opening the way for an in vivo optical diagnosis.