Analyzing the serum of hemodialysis patients with end-stage chronic kidney disease by means of the combination of SERS and machine learning

Bratchenko, Liudmila; Zead, Sahar; Тупикова, Е. Н.; Konovalova, Daria; Lebedev, P. A.; Zakharov, V. F.; Bratchenko, Ivan А.

doi:10.1364/boe.455549

Cited by 13 publications

(7 citation statements)

References 54 publications

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“… 15 Although SERS application is possible for disease detection due to the lower price of the device, the consumables used for SERS were more expensive, caused by the signal amplification generated through the use of plasmonic nanostructures. 33 And the high-quality requirements of the nanostructure might bring a high cost, which made SERS not appropriate for the IPNs screening.…”

Section: Discussionmentioning

confidence: 99%

Serum laser Raman spectroscopy as a potential diagnostic tool to discriminate the benignancy or malignancy of pulmonary nodules

Luo¹,

Zu²,

Li³

et al. 2023

iScience

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Section: Discussionmentioning

confidence: 99%

Serum laser Raman spectroscopy as a potential diagnostic tool to discriminate the benignancy or malignancy of pulmonary nodules

Luo¹,

Zu²,

Li³

et al. 2023

iScience

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“…The machine learning models PLS-DA and CNN were used to identify different stages of kidney malfunction in dialysis patients by using serum analysis by SERS. The CNN model achieved an accuracy of 96%, which is better than that of PLS-DA, with 84% [ 387 ]. The SVM outperformed other techniques in the identification of cyanobacteria, using SERS spectra of mutant and wild-type strains [ 388 ].…”

Section: Machine Learning In Sers-based Biosensingmentioning

confidence: 99%

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

2023

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Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS’s full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

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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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Analyzing the serum of hemodialysis patients with end-stage chronic kidney disease by means of the combination of SERS and machine learning

Cited by 13 publications

References 54 publications

Serum laser Raman spectroscopy as a potential diagnostic tool to discriminate the benignancy or malignancy of pulmonary nodules

Serum laser Raman spectroscopy as a potential diagnostic tool to discriminate the benignancy or malignancy of pulmonary nodules

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

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

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