Raman Spectroscopy as a New Biochemical Diagnostic Tool

Uskoković‐Marković, Snežana; Jelikić-Stankov, Milena; Holclajtner-Antunović, Ivanka; Đurđević, Predrag

doi:10.2478/jomb-2013-0004

Cited by 12 publications

(12 citation statements)

References 38 publications

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“…2,3,5,7,13,23 The Raman method would allow the simultaneous and rapid identification of biochemical components and their quantitative determination, including glucose, acetone, creatinine, urea, lipids, uric acid, and total blood protein. 22,44 The potential for the characterization of samples associated with the possibility of in situ analysis makes this analytical technique a promising tool for the evaluation of biochemical constituents. The results indicate that RS may prove to be a promising technique for the quantification of urea and creatinine in midstream urine, which could be used for the early diagnosis of renal changes at different stages of renal disease.…”

Section: Quantitative Analysis Of Urine Samples From Diabetic and Hypmentioning

confidence: 99%

Quantifying creatinine and urea in human urine through Raman spectroscopy aiming at diagnosis of kidney disease

et al. 2016

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Due to their importance in the regulation of metabolites, the kidneys need continuous monitoring to check for correct functioning, mainly by urea and creatinine urinalysis. This study aimed to develop a model to estimate the concentrations of urea and creatinine in urine by means of Raman spectroscopy (RS) that could be used to diagnose kidney disease. Midstream urine samples were obtained from 54 volunteers with no kidney complaints. Samples were subjected to a standard colorimetric assay of urea and creatinine and submitted to spectroscopic analysis by means of a dispersive Raman spectrometer (830 nm, 350 mW, 30 s). The Raman spectra of urine showed peaks related mainly to urea and creatinine. Partial least squares models were developed using selected Raman bands related to urea and creatinine and the biochemical concentrations in urine measured by the colorimetric method, resulting in r = 0.90 and 0.91 for urea and creatinine, respectively, with root mean square error of cross-validation (RMSEcv) of 312 and 25.2 mg/dL, respectively. RS may become a technique for rapid urinalysis, with concentration errors suitable for population screening aimed at the prevention of renal diseases.

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Section: Quantitative Analysis Of Urine Samples From Diabetic and Hypmentioning

confidence: 99%

Quantifying creatinine and urea in human urine through Raman spectroscopy aiming at diagnosis of kidney disease

et al. 2016

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“…This vibration is attributed to the C—N bond of quaternary ammonium groups of phosphatidylcholines and sphingomyelins, which are major components of cell membranes. 44 Even though very weak, its presence implies that there are chemical changes occurring at cell membranes with changes from noncancerous to cancerous states. Similar observations of sphingomyelin overexpression in a more complicated environment of lumen cancerous breast ducts have also been reported in the literature.…”

Section: Resultsmentioning

confidence: 99%

Label-Free Raman Imaging to Monitor Breast Tumor Signatures

Manciu

Ciubuc

Parra

et al. 2016

Technol Cancer Res Treat

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Although not yet ready for clinical application, methods based on Raman spectroscopy have shown significant potential in identifying, characterizing, and discriminating between noncancerous and cancerous specimens. Real-time and accurate medical diagnosis achievable through this vibrational optical method largely benefits from improvements in current technological and software capabilities. Not only is the acquisition of spectral information now possible in milliseconds and analysis of hundreds of thousands of data points achieved in minutes, but Raman spectroscopy also allows simultaneous detection and monitoring of several biological components. Besides demonstrating a significant Raman signature distinction between nontumorigenic (MCF-10A) and tumorigenic (MCF-7) breast epithelial cells, our study demonstrates that Raman can be used as a label-free method to evaluate epidermal growth factor activity in tumor cells. Comparative Raman profiles and images of specimens in the presence or absence of epidermal growth factor show important differences in regions attributed to lipid, protein, and nucleic acid vibrations. The occurrence, which is dependent on the presence of epidermal growth factor, of new Raman features associated with the appearance of phosphothreonine and phosphoserine residues reflects a signal transduction from the membrane to the nucleus, with concomitant modification of DNA/RNA structural characteristics. Parallel Western blotting analysis reveals an epidermal growth factor induction of phosphorylated Akt protein, corroborating the Raman results. The analysis presented in this work is an important step toward Raman-based evaluation of biological activity of epidermal growth factor receptors on the surfaces of breast cancer cells. With the ultimate future goal of clinically implementing Raman-guided techniques for the diagnosis of breast tumors (e.g., with regard to specific receptor activity), the current results just lay the foundation for further label-free optical tools to diagnose the disease.

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“…n at each wavelength are obtained by calculating the mean values A λ 1 and B λ 1 of the two parts in the period according to (4) and (5). The superposition of the sampling points of each part in the period further reduces the influence of random noise and the possibility of singular value generation.…”

Section: B ''Superposition Averaging'' Strategymentioning

confidence: 99%

“…Non-invasive blood component detection has great value in human health detection with its advantages of realtime, non-invasive, environmentally friendly, high-precision and continuous measurement, and has become a hot issue in biomedical engineering research [1], [2]. The current non-invasive detection methods for blood components can be roughly divided into two categories: Optical methods [3]- [5] and Non-optical methods [6]- [8]. The optical method stands out among many blood component detection methods with its unique non-invasive, environmental…”

Section: Introductionmentioning

confidence: 99%

Dual-Mean Extraction Method of Dynamic Spectrum for Suppressing Random Noise and Coarse Error

Tang

Yang

et al. 2019

IEEE Access

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Dynamic spectrum (DS) can greatly reduce the effects of individual differences and measurement environment by extracting the equivalent absorbance of pulsating arterial blood at multiple wavelengths. DS is expected to achieve non-invasive detection of human blood components. This paper proposes a dual-mean extraction method to reduce random noise and coarse error caused by the jitter of the subject in the acquisition process. The ''superposition averaging'' strategy is adopted twice in this method to reduce the interference of random noise, and a more rigorous standard is used to screen and eliminate coarse error. The single-trial extraction method, the optimizing differential extraction method and the dual-mean extraction method were used to extract the DS from the clinical data of 231 volunteers respectively. The Radial basis function (RBF) neural network was used to model the experimental data of the three extraction results. The difference between the correlation coefficients of calibration set and prediction set was used to evaluate the overfitting degree of the model. The results showed that, the correlation coefficients of prediction set of the data extracted by the dual-mean method reached 0.793, which was higher than the 0.714 of the single-trial method and the 0.749 of the optimizing differential method. And the overfitting degree of the model built by the data of the dual-mean method is the lowest in the next 10 sets of repeated modeling experiments. Compared with the single trial and optimizing differential extraction method, the dual-mean extraction method is better in suppressing random noise and coarse error in DS, and can obtain spectral data with higher signal-to-noise ratio.

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Raman Spectroscopy as a New Biochemical Diagnostic Tool

Cited by 12 publications

References 38 publications

Quantifying creatinine and urea in human urine through Raman spectroscopy aiming at diagnosis of kidney disease

Quantifying creatinine and urea in human urine through Raman spectroscopy aiming at diagnosis of kidney disease

Label-Free Raman Imaging to Monitor Breast Tumor Signatures

Dual-Mean Extraction Method of Dynamic Spectrum for Suppressing Random Noise and Coarse Error

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