Correlation between near‐infrared Raman spectroscopy and the histopathological analysis of atherosclerosis in human coronary arteries

Silveira, Landulfo; Sathaiah, S.; Zângaro, Renato Amaro; Pacheco, Marcos Tadeu Tavares; Chavantes, Maria Cristina; Pasqualucci, Carlos Augusto

doi:10.1002/lsm.10053

Cited by 132 publications

(95 citation statements)

References 28 publications

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“…A widely used MVA method to analyze Raman spectra is PCA, because it is readily implemented in most standard software packages for data acquisition and data analysis. [32][33][34] A comprehensive review of PC analysis of Raman spectra can be found elsewhere. 35 PCA applies a set of linear-orthogonal transformations to the data set, which identifies correlated variables in the data and assigns them to new, uncorrelated variables, the socalled PCs.…”

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confidence: 99%

Methods and Applications of Raman Microspectroscopy to Single-Cell Analysis

2013

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Raman spectroscopy is a powerful biochemical analysis technique that allows for the dynamic characterization and imaging of living biological cells in the absence of fluorescent stains. In this review, we summarize some of the most recent developments in the noninvasive biochemical characterization of single cells by spontaneous Raman scattering. Different instrumentation strategies utilizing confocal detection optics, multispot, and line illumination have been developed to improve the speed and sensitivity of the analysis of single cells by Raman spectroscopy. To analyze and visualize the large data sets obtained during such experiments, sophisticated multivariate statistical analysis tools are necessary to reduce the data and extract components of interest. We highlight the most recent applications of single cell analysis by Raman spectroscopy and their biomedical implications that have enabled the noninvasive characterization of specific metabolic states of eukaryotic cells, the identification and characterization of stem cells, and the rapid identification of bacterial cells. We conclude the article with a brief look into the future of this rapidly evolving research area.

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confidence: 99%

Methods and Applications of Raman Microspectroscopy to Single-Cell Analysis

2013

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“…Few analytical methods satisfy this requirement and are sensible enough to study details of tissue compositions and structures. Among those methods, molecular vibrational spectroscopy is a potential technique to diagnose and study the evolution of human diseases, such as atherosclerosis [5] and precancerous and cancerous lesions [4,[6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Many studies have been performed as a tentative to correlate differences in the Raman spectrum to the tissue pathology [3][4][5][6][7][8][9][10][11]. FT-Raman have been used as a diagnostic in a variety of human diseases, mainly constructing a database and identifying main spectral features [8,15].…”

Section: Introductionmentioning

confidence: 99%

FT‐Raman spectroscopy study for skin cancer diagnosis

Nunes

Martin

Silveira

et al. 2003

Journal of Spectroscopy

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Abstract. Early detection of cancer is still a great challenge in clinical oncology. Recently, Raman spectroscopy has been used for skin lesion detection. FT-Raman spectroscopy is a modern analytical tool and its use for cancer diagnosis will lead to several advantages for the patient as, for example, real time and less invasive diagnosis. The primary objective of this work was to use FT-Raman spectroscopy to detect spectral changes between benign and malignant (basal cell carcinoma -BCC) skin tissues. Those spectral changes can provide important information about the biochemical alterations between these two types of tissues. We have analyzed by FT-Raman eight set of samples histopathologically diagnosed as BCC and made a comparison with five set of samples diagnosed as benign tissue. We have found that the main spectral differences between these samples were in the shift region of 1220-1300 cm −1 and 1640-1680 cm −1 . The vibration bands in these regions correspond to the amide III and to the amide I vibrations, respectively. Principal components analysis applied over all 13 samples could identify tissue type with 100% of sensitivity and specificity.

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“…The first PCs are related to the larger variation of the data and the last PCs carry only noise [31]. Discriminant analysis is used to separate samples with similar characteristics into well-defined groups or classes.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic model based on Raman spectra of normal, hyperplasia and prostate adenocarcinoma tissuesin vitro

et al. 2011

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Abstract. This study evaluated the use of Raman spectroscopy to identify the spectral differences between normal (N), benign hyperplasia (BPH) and adenocarcinoma (CaP) in fragments of prostate biopsies in vitro with the aim of developing a spectral diagnostic model for tissue classification. A dispersive Raman spectrometer was used with 830 nm wavelength and 80 mW excitation. Following Raman data collection and tissue histopathology (48 fragments diagnosed as N, 43 as BPH and 14 as CaP), two diagnostic models were developed in order to extract diagnostic information: the first using PCA and Mahalanobis analysis techniques and the second one a simplified biochemical model based on spectral features of cholesterol, collagen, smooth muscle cell and adipocyte. Spectral differences between N, BPH and CaP tissues, were observed mainly in the Raman bands associated with proteins, lipids, nucleic and amino acids. The PCA diagnostic model showed a sensitivity and specificity of 100%, which indicates the ability of PCA and Mahalanobis distance techniques to classify tissue changes in vitro. Also, it was found that the relative amount of collagen decreased while the amount of cholesterol and adipocyte increased with severity of the disease. Smooth muscle cell increased in BPH tissue. These characteristics were used for diagnostic purposes.

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Correlation between near‐infrared Raman spectroscopy and the histopathological analysis of atherosclerosis in human coronary arteries

Abstract: An algorithm using PCA and discriminant analysis based on m-distance has been developed and successfully applied to diagnose coronary artery disease by NIRS obtaining good sensitivity and specificity for each tissue category.

Cited by 132 publications

References 28 publications

Methods and Applications of Raman Microspectroscopy to Single-Cell Analysis

Methods and Applications of Raman Microspectroscopy to Single-Cell Analysis

FT‐Raman spectroscopy study for skin cancer diagnosis

Diagnostic model based on Raman spectra of normal, hyperplasia and prostate adenocarcinoma tissuesin vitro

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