Vibrational Spectroscopy in Urine Samples as a Medical Tool: Review and Overview on the Current State-of-the-Art

Vigo, Francesco; Tozzi, Alessandra; Disler, Muriel; Gisi, Alessia; Kavvadias, Vasileios; Kavvadias, Tilemachos

doi:10.3390/diagnostics13010027

Cited by 3 publications

(5 citation statements)

References 71 publications

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“…This paper describes the use of a portable RS to detect the changes in skin biochemical composition in the presence of the CHF disease. Several research teams [39][40][41][42][43][44][45][46][47] have analyzed the possibility of optical methods to detect different diseases based on the study of human biochemicals mainly in urine, blood serum, plasma blood or whole blood. Among various optical methods, RS has demonstrated its prospects to track changes in chemical composition with the accuracy close to the current laboratory techniques.…”

Section: Advantages and Disadvantages Of The Proposed Rs Approach For...mentioning

confidence: 99%

Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis

Khristoforova

Bratchenko

Skuratova

et al. 2023

Journal of Biophotonics

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This work aims at studying Raman spectroscopy in combination with chemometrics as an alternative fast noninvasive method to detect chronic heart failure (CHF) cases. Optical analysis is focused on the changes in the spectral features associated with the biochemical composition changes of skin tissues. A portable spectroscopy setup with the 785 nm excitation wavelength was used to record skin Raman features. In this in vivo study, 127 patients and 57 healthy volunteers were involved in measuring skin spectral features by Raman spectroscopy. The spectral data were analyzed with a projection on the latent structures and discriminant analysis. 202 skin spectra of patients with CHF and 90 skin spectra of healthy volunteers were classified with 0.888 ROC AUC for the 10-fold cross validated algorithm. To identify CHF cases, the performance of the proposed classifier was verified by means of a new test set that is equal to 0.917 ROC AUC.

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Section: Advantages and Disadvantages Of The Proposed Rs Approach For...mentioning

confidence: 99%

Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis

Khristoforova

Bratchenko

Skuratova

et al. 2023

Journal of Biophotonics

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“…The separated biomarkers can then be detected using a variety of methods, including UV detection, fluorescence detection, or MS [21][22][23]. Also, some other spectroscopic techniques were applied for urinary metabolic biomarkers characterisation, such as Fourier transform infrared (FTIR) spectroscopy [24][25][26][27] and Raman spectroscopy [28][29][30]. Still, the most used method for biomarker analysis is enzyme-linked immunosorbent assay (ELISA) [31][32][33] and fluorescence polarisation immunoassay (FPIA) [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the complexity of biomolecules, their IR spectra are challenging to analyse because of overlapping IR bands [112]. Therefore, they are often combined using computers and statistical computational systems, which help to obtain spectra quickly and make them easily interpretable [27]. Analytical techniques that rely on molecular vibrations are Raman spectroscopy, Fourier transform infrared (FTIR) spectroscopy and THz spectroscopy.…”

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confidence: 99%

“…Fluorescence spectroscopy can be used to identify the biomolecules present in the sample and can be used to detect changes in the biomolecule structure due to disease or other factors. Several IR spectroscopic methods are used for biomarker detection in urine samples in far-, mid-and near-infrared regions [27,[114][115][116]. The IR spectral range covers wavelengths from 780 nm to 1000 µm, which can be further subdivided into the near-infrared (NIR) range from 780 nm to 2500 nm, the mid-infrared (MIR) range from 2500 nm to 50 µm and the far-infrared (FIR) range from 50 µm to 1000 µm [112].…”

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confidence: 99%

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Urinary Metabolic Biomarker Profiling for Cancer Diagnosis by Terahertz Spectroscopy: Review and Perspective

Abina,

Korošec,

Puc

et al. 2023

Photonics

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In the last decade, terahertz (THz) technologies have been introduced to the detection, identification, and quantification of biomolecules in various biological samples. This review focuses on substances that represent important biomarkers in the urine associated with various cancers and their treatments. From a diagnostic point of view, urine liquid biopsy is particularly important because it allows the non-invasive and rapid collection of large volumes of samples. In this review, the THz spectral responses of substances considered metabolic biomarkers in urine and obtained in previous studies are collected. In addition, the findings from the relatively small number of prior studies that have already been carried out on urine samples are summarised. In this context, we also present the different THz methods used for urine analysis. Finally, a brief discussion is given, presenting perspectives for future research in this field, interpreted based on the results of previous studies. This work provides important information on the further application of THz techniques in biomedicine for detecting and monitoring urinary biomarkers for various diseases, including cancer.

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“…The revealed spectrum represents a faithful representation of the unique molecular characteristics investigated. This rapid, non-destructive, minimally invasive, and relatively inexpensive technique has shown to be promising in detecting biomarkers in different samples [14]. Hence, with the emerging progress in technology and machine learning, the analysis and classification of multiple parameters derived from spectroscopic analysis have been facilitated, and its potential exponentially elevated.…”

Section: Introductionmentioning

confidence: 99%

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia

Raposo-Neto,

Kowalski-Neto,

Luiz

et al. 2023

JPM

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The use of non-invasive tools in conjunction with artificial intelligence (AI) to detect diseases has the potential to revolutionize healthcare. Near-infrared spectroscopy (NIR) is a technology that can be used to analyze biological samples in a non-invasive manner. This study evaluated the use of NIR spectroscopy in the fingertip to detect neutropenia in solid-tumor oncologic patients. A total of 75 patients were enrolled in the study. Fingertip NIR spectra and complete blood counts were collected from each patient. The NIR spectra were pre-processed using Savitzky–Golay smoothing and outlier detection. The pre-processed data were split into training/validation and test sets using the Kennard–Stone method. A toolbox of supervised machine learning classification algorithms was applied to the training/validation set using a stratified 5-fold cross-validation regimen. The algorithms included linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), and support vector machines (SVMs). The SVM model performed best in the validation step, with 85% sensitivity, 89% negative predictive value (NPV), and 64% accuracy. The SVM model showed 67% sensitivity, 82% NPV, and 57% accuracy on the test set. These results suggest that NIR spectroscopy in the fingertip, combined with machine learning methods, can be used to detect neutropenia in solid-tumor oncology patients in a non-invasive and timely manner. This approach could help reduce exposure to invasive tests and prevent neutropenic patients from inadvertently undergoing chemotherapy.

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Vibrational Spectroscopy in Urine Samples as a Medical Tool: Review and Overview on the Current State-of-the-Art

Cited by 3 publications

References 71 publications

Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis

Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis

Urinary Metabolic Biomarker Profiling for Cancer Diagnosis by Terahertz Spectroscopy: Review and Perspective

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia

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