Renato Amaro Zângaro

Rapid multiexcitation fluorescence spectroscopy system for in vivo tissue diagnosis

We have designed, fabricated, and tested a compact, transportable, excitation-emission spectrofluorimeter with optical-fiber light delivery and collection for use in rapid analysis of tissues in a clinical setting. This system provides up to eleven different excitation wavelengths, permitting collection of all the corresponding emission spectra in approximately 600 ms. It uses a N(2) laser that pumps a sequence of dyes placed in cuvettes on a rotating wheel. A white-light excitation source permits acquisition of the tissues diffuse reflectance spectrum on each cycle. Return fluorescence and reflected light are dispersed by a small spectrograph and detected by a photodiode-array detector. The system can collect a single-shot spectrum from biological tissue with a signal-to-noise ratio in excess of 50:1.

Raman spectroscopy study of atherosclerosis in human carotid artery.

Fourier-transform (FT)-Raman spectroscopy has been used for identification and evaluation of human artherosclerotic lesions, providing biochemical information on arteries. In this work, fragments of human carotid arteries postmortem were analyzed using a FT-Raman spectrometer operating at an excitation wavelength of 1064 nm, power of 200 mW, and spectral resolution of 4 cm(-1). A total of 75 carotid fragments were spectroscopically scanned and FT-Raman results were compared with histopathology. Discriminant analysis using Mahalanobis distance was applied over principal components scores for tissue classification into three categories: nonatherosclerotic, atherosclerotic plaque without calcification and with calcification. Nonatherosclerotic artery, atherosclerotic plaque, and calcified plaque exhibit spectral signatures related to biochemicals presented in each tissue type, such as bands of collagen and elastin (proteins), cholesterol and its esters, and calcium hydroxyapatite and carbonate apatite, respectively. Spectra of nonatherosclerotic artery were then classified into two groups: normal and discrete diffuse thickening of the intima layer (first group) and moderate and intense diffuse thickening of the intima layer (second group). FT-Raman could identify and classify the tissues found in the atherosclerotic process in human carotid in vitro and had the ability to identify alterations to the diffuse thickening of the intima layer and classify it depending on the intensity of the thickening.

Rapid Identification of Bacterial Species by Fluorescence Spectroscopy and Classification Through Principal Components Analysis

This work presents the development of a method for rapid bacterial identification based on the autofluorescence spectrum. It was demonstrated differences in the autofluorescence spectrum in three bacterial species and the subsequent separation, through the Principal Components Analysis (PCA) technique, in groups with high likeness, that could identify the bacteria in less than 10 min. Fluorescence spectra of 60 samples of 3 different bacterial species (Escherichia coli, EC, Enterococcus faecalis, EF and Staphylococcus aureus, SA), previously identified by automated equipment Mini API, were collected in 10 excitation wavelengths from 330 to 510 nm. The PCA technique applied to the fluorescence spectra showed that bacteria species could be identified with sensitivity and specificity higher than 90% according to differences that occur within the spectra with excitation of 410 nm and 430 nm. This work presented a method of bacterial identification of three more frequent and more clinically significant species based on the autofluorescence spectra in the excitation wavelengths of 410 and 430 nm and the classification of the spectra in three groups using PCA. The results demonstrated that the bacterial identification is very efficient with such methodology. The proposed method is rapid, ease to perform and low cost compared to standard methods.

Analysis of Near-infrared Raman Spectroscopy as a New Technique for a Transcutaneous Non-invasive Diagnosis of Blood Components

Abstract. Near-infrared Raman spectroscopy can be a new technique for physical evaluations, allowing the measurement of lactic acid concentrations, in blood or muscles, during the physical activity in a transcutaneous non-invasive way. Lactic acid accumulation in the human body is one of the factors that leads to fatigue and therefore it should be continually monitored during physical training. Our proposal is to use Raman spectroscopy to monitor the lactic acid present in an athlete without interrupting his exercise for sample collection. The experimental set-up for Raman spectroscopy comprised a near infrared laser at 830 nm, a Kaiser f/1.8 spectrometer and a liquid nitrogen cooled CCD detector. The radiation from the exciting laser is blocked in the collecting system by Kaiser holographic filters. A personal computer controls the entire system, saving and processing the Raman spectra. Experiments were undertaken to verify the presence of lactic acid in the Raman spectra of solutions of lactic acid in human serum and in blood from a Wistar rat. After these two experiments, another was developed in vivo in a Wistar rat, injecting intraperitoneally 1 ml of a 0.12 mol/l lactic acid aqueous solution. An optical fibre catheter touching the skin of the rat groin, over the ileac vein collected the Raman signal. The presence of lactic acid was detected inside a live organism, in a transcutaneous non-invasive way. The minimum lactic acid concentration that the equipment can detect was also studied. An experiment was undertaken for that purpose, in which the laser illuminated directly a quartz cuvette containing solutions with decreasing lactic acid concentrations up to values near to the physiological level in the human body. The results indicated that the technique can be suitable for the physical evaluation of athletes.

Discriminating model for diagnosis of basal cell carcinoma and melanoma in vitro based on the Raman spectra of selected biochemicals

Abstract. Raman spectroscopy has been employed to identify differences in the biochemical constitution of malignant [basal cell carcinoma (BCC) and melanoma (MEL)] cells compared to normal skin tissues, with the goal of skin cancer diagnosis. We collected Raman spectra from compounds such as proteins, lipids, and nucleic acids, which are expected to be represented in human skin spectra, and developed a linear least-squares fitting model to estimate the contributions of these compounds to the tissue spectra. We used a set of 145 spectra from biopsy fragments of normal (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues, collected using a near-infrared Raman spectrometer (830 nm, 50 to 200 mW, and 20 s exposure time) coupled to a Raman probe. We applied the best-fitting model to the spectra of biochemicals and tissues, hypothesizing that the relative spectral contribution of each compound to the tissue Raman spectrum changes according to the disease. We verified that actin, collagen, elastin, and triolein were the most important biochemicals representing the spectral features of skin tissues. A classification model applied to the relative contribution of collagen III, elastin, and melanin using Euclidean distance as a discriminator could differentiate normal from BCC and MEL.

Raman spectroscopy: A powerful technique for biochemical analysis and diagnosis

The present work focuses on the recent applications of Raman spectroscopy (RS) on biochemical analysis and diagnosis of several biological materials with or without pathological alterations. Important published works about Raman spectroscopy and its use for medical applications were critically reviewed, including articles form our group in order to evaluate the state of the art of the subject. The potential for sample characterization with RS associated to the possibility of analysisin situ makes this instrumental technique in a very auspicious tool of biochemical analysis. RS can promote a significant improvement in the chemical identification and characterization of biological systems, clinical diagnosis and prognosis regarding several diseases and quality of life of innumerous patients. The spectroscopic evaluation is based on the analysis of the Raman spectrum regarding the identification of fingerprint bands of main biological macromolecules, such as nucleic acids, proteins and fat, present in the tissue structure. This review evaluates the employment of RS in diagnosing such pathological manifestations as well as the efforts focused on the instrumental development to biomedical applications. Furthermore, advantages and limitations of this kind of approach are discussed in order to improve the biochemical analysis and diagnosis of several diseases.

Discrimination of Basal Cell Carcinoma and Melanoma from Normal Skin Biopsies in Vitro Through Raman Spectroscopy and Principal Component Analysis

OBJECTIVE Raman spectroscopy has been employed to discriminate between malignant (basal cell carcinoma [BCC] and melanoma [MEL]) and normal (N) skin tissues in vitro, aimed at developing a method for cancer diagnosis. BACKGROUND DATA Raman spectroscopy is an analytical tool that could be used to diagnose skin cancer rapidly and noninvasively. METHODS Skin biopsy fragments of ≈ 2 mm(2) from excisional surgeries were scanned through a Raman spectrometer (830 nm excitation wavelength, 50 to 200 mW of power, and 20 sec exposure time) coupled to a fiber optic Raman probe. Principal component analysis (PCA) and Euclidean distance were employed to develop a discrimination model to classify samples according to histopathology. In this model, we used a set of 145 spectra from N (30 spectra), BCC (96 spectra), and MEL (19 spectra) skin tissues. RESULTS We demonstrated that principal components (PCs) 1 to 4 accounted for 95.4% of all spectral variation. These PCs have been spectrally correlated to the biochemicals present in tissues, such as proteins, lipids, and melanin. The scores of PC2 and PC3 revealed statistically significant differences among N, BCC, and MEL (ANOVA, p<0.05) and were used in the discrimination model. A total of 28 out of 30 spectra were correctly diagnosed as N, 93 out of 96 as BCC, and 13 out of 19 as MEL, with an overall accuracy of 92.4%. CONCLUSIONS This discrimination model based on PCA and Euclidean distance could differentiate N from malignant (BCC and MEL) with high sensitivity and specificity.

Differentiating Normal and Basal Cell Carcinoma Human Skin Tissues In Vitro Using Dispersive Raman Spectroscopy: A Comparison Between Principal Components Analysis and Simplified Biochemical Models

OBJECTIVE Raman spectroscopy has been used to detect spectral differences between normal and basocellular cell carcinoma (BCC) skin tissues that are related to biochemical alterations between tissues. BACKGROUND DATA Raman spectroscopy is an analytic tool that could detect biochemical alterations in tissues, and its use would lead to real-time and less-invasive cancer diagnosis. METHODS Raman spectra from human tissue fragments (normal and BCC) were obtained in a dispersive, near-infrared Raman spectrometer (laser parameters: 830 nm, 80 mW) with a CCD detector. Spectral changes between normal and BCC were analyzed with a principal components analysis (PCA) algorithm and a simplified biochemical model based on the relative amount of collagen and cell fat extracted from tissue Raman spectra. RESULTS Main spectral differences between these samples were in the region of 800 to 1,000 per centimeter and 1,200 to 1,300 per centimeter, corresponding to vibrational bands from lipids and proteins (C-C bonds and amide III, respectively). The diagnostic algorithm based on PCA and Mahalanobis distance applied to the scores of principal components vectors PC1 and PC2 could identify tissue with sensitivity and specificity of 89% and 93%, respectively, for the training group and 96% and 92% for the prospective group. The simplified biochemical model for collagen amount had sensitivity and specificity of 95% and 83% for the training group and 87% and 92% for the prospective group. CONCLUSIONS Raman spectroscopy could differentiate between normal and BCC tissues in both the PCA and biochemical models, showing higher sensitivity and specificity for the PCA model, although the simplified biochemical model is easier to implement.

Near-Infrared Raman Spectroscopy of Human Coronary Arteries: Histopathological Classification Based on Mahalanobis Distance

OBJECTIVE In this study, near-infrared Raman spectroscopy (NIRS) was used for evaluation of human atherosclerotic lesions using a simple algorithm based on discriminant analysis. The Mahalanobis distance was used to classify the clustered spectral features extracted from NIRS of a total of 111 arterial fragments of human coronary arteries. BACKGROUND DATA Raman spectroscopy has been used for diagnosis of a variety of diseases. For real-time applications, it is important to have a simple algorithm that could perform fast data acquisition and analysis. The ultimate goal is to obtain a feasible diagnosis, which discriminates various atherosclerotic lesions with high sensitivities and specificities. MATERIALS AND METHODS Non-atherosclerotic (NA) arteries, atherosclerotic plaques without calcification (NC), and atherosclerotic plaques with classification (C) were obtained and scanned with an NIR Raman spectrometer with 830-nm laser excitation. An algorithm based on the discriminant analysis using the Mahalanobis distance of the clustered spectral features was used for tissue classification into three categories: Na, NC, and C. RESULTS Human coronary arteries exhibit different spectral signatures depending on different bio-chemicals present in each tissue type such as collagen, cholesterol, and calcium hydroxyapatite, respectively. It is shown that our algorithm has a maximum sensitivity and specificity of 85% and 89%, respectively, for the diagnosis of the NA tissue type, 85% and 89% for the NC tissue type, and 100% and 100% for the C tissue type. CONCLUSION An algorithm (with a minimum of mathematical and computational requirements) based on the discriminant analysis of spectral features has been developed to classify atherosclerotic lesions with high sensitivities and specificities.

Using the laser-induced fluorescence spectroscopy in the differentiation between normal and neoplastichuman breast tissue.

This article reports results of the in vitro study for potential evaluation of the laser-induced fluorescence spectroscopy in the differentiation between normal and neoplastic human breast tissue. A coumarine dye laser pumped by nitrogen laser generated an excitation light centered at 458 nm. In order to collect the fluorescence signal was used an optical fiber catheter coupled to a spectrometer and CCD detector. Fluorescence spectra were recorded from normal and neoplastic (benign and malignant) human breast tissue, adding up 94 different areas. The discrimination between normal and neoplasm groups reach a sensitivity and specificity of 100%.