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

Khristoforova, Yulia А.; Bratchenko, Lyudmila A.; Skuratova, M. A.; Lebedeva, Elena A; Lebedev, P. A.; Bratchenko, Ivan А.

doi:10.1002/jbio.202300016

Cited by 11 publications

(2 citation statements)

References 57 publications

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“…Aiming at checking the performance of the built models on the new data, we considered splitting the data in two sets used for training and validation, respectively, with the main goal to compare the discrimination accuracy features on these two sets [71,72]. In this sense, the data were separated in two sets, denoted map 1 and map 2.…”

Section: Multivariate Analysis Of Normalized Datamentioning

confidence: 99%

Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques

Buhas,

Toma,

Beauval

et al. 2024

IJMS

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The advent of Surface-Enhanced Raman Scattering (SERS) has enabled the exploration and detection of small molecules, particularly in biological fluids such as serum, blood plasma, urine, saliva, and tears. SERS has been proposed as a simple diagnostic technique for various diseases, including cancer. Renal cell carcinoma (RCC) ranks as the sixth most commonly diagnosed cancer in men and is often asymptomatic, with detection occurring incidentally. The onset of symptoms typically aligns with advanced disease, aggressive histology, and unfavorable prognosis, and therefore new methods for an early diagnosis are needed. In this study, we investigated the utility of label-free SERS in urine, coupled with two multivariate analysis approaches: Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) and Support Vector Machine (SVM), to discriminate between 50 RCC patients and 44 healthy donors. Employing LDA-PCA, we achieved a discrimination accuracy of 100% using 13 principal components, and an 88% accuracy in discriminating between different RCC stages. The SVM approach yielded a training accuracy of 100%, a validation accuracy of 99% for discriminating between RCC and controls, and an 80% accuracy for discriminating between stages. The comparative analysis of raw and normalized SERS spectral data shows that while raw data disclose relative concentration variations in urine metabolites between the two classes, the normalization of spectral data significantly improves the accuracy of discrimination. Moreover, the selection of principal components with markedly distinct scores between the two classes serves to alleviate overfitting risks and reduces the number of components employed for discrimination. We obtained the accuracy of the discrimination between the RCC patients cases and healthy donors of 90% for three PCs and a linear discrimination function, and a 88% accuracy of discrimination between stages using six PCs, mitigating practically the risk of overfitting and increasing the robustness of our analysis. Our findings underscore the potential of label-free SERS of urine in conjunction with chemometrics for non-invasive and early RCC detection.

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Section: Multivariate Analysis Of Normalized Datamentioning

confidence: 99%

Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques

Buhas,

Toma,

Beauval

et al. 2024

IJMS

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“…In this context, Raman spectroscopy can have greater potential in comparison to the laboratory procedures currently utilized in clinical settings. On the other hand, several studies [30,38] have reported that Raman spectral features analyzed with different mathematical methods (chemometrics, machine learning, artificial intelligence, etc.) are able to detect a disease by means of the meaningful spectral differences between the disease and control cases without identifying the exact biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review

Khristoforova,

Bratchenko,

Bratchenko

2023

IJMS

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Raman spectroscopy is a widely developing approach for noninvasive analysis that can provide information on chemical composition and molecular structure. High chemical specificity calls for developing different medical diagnostic applications based on Raman spectroscopy. This review focuses on the Raman-based techniques used in medical diagnostics and provides an overview of such techniques, possible areas of their application, and current limitations. We have reviewed recent studies proposing conventional Raman spectroscopy and surface-enhanced Raman spectroscopy for rapid measuring of specific biomarkers of such diseases as cardiovascular disease, cancer, neurogenerative disease, and coronavirus disease (COVID-19). As a result, we have discovered several most promising Raman-based applications to identify affected persons by detecting some significant spectral features. We have analyzed these approaches in terms of their potentially diagnostic power and highlighted the remaining challenges and limitations preventing their translation into clinical settings.

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Skin dehydration monitoring with optical spectroscopy allows assessment of water content in the organism: Thermal and physical loads, diuretic therapy

Davydov,

Budylin,

Baev

et al. 2024

Journal of Biophotonics

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This study investigates the relationship between body hydration levels and skin hydration using spatially resolved diffuse reflectance spectroscopy. The research involved monitoring skin dehydration and rehydration under various conditions, including thermal and physical loads on healthy volunteers, and diuretic therapy in patients with edema syndrome. Findings indicate a correlation between body mass reduction and skin hydration: a 1% loss in body mass corresponds to a 10% decrease in skin hydration. During thermal stress, water absorption at 970 nm decreased monotonically without recovery. Physical activity resulted in approximately 10% changes in skin water content within 20 min, followed by rehydration. Patients with edema syndrome exhibited the most substantial decrease in water absorption amplitude, at nearly 30%, during diuretic treatment. These results support optical spectroscopy as a non‐invasive tool for assessing body hydration, with implications for developing portable hydration monitoring devices for clinical and sports applications.

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

Cited by 11 publications

References 57 publications

Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques

Label-Free SERS of Urine Components: A Powerful Tool for Discriminating Renal Cell Carcinoma through Multivariate Analysis and Machine Learning Techniques

Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review

Skin dehydration monitoring with optical spectroscopy allows assessment of water content in the organism: Thermal and physical loads, diuretic therapy

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