The freeze-thaw cycle effect on blood serum autofluorescence, Raman and SERS: implications for sample classification and disease diagnostics DOI

Polina K. Nurgalieva,

Boris P. Yakimov, Olga D. Parashchuk

et al.

The Analyst, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 30, 2024

The alterations in blood serum optical signal caused by a freeze–thaw cycle do not affect patient classification or disease diagnosis. may be performed prior to spectroscopy analysis clinical diagnostics.

Language: Английский

Deep learning of surface-enhanced Raman spectroscopy data for multiple sclerosis diagnostics DOI
Alexander Zakharov, Ivan А. Bratchenko, Lyudmila A. Bratchenko

et al.

The European Physical Journal Special Topics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Language: Английский

Citations

1
Comparative Study Into the Effect of Detector Noises and Sensitivity on the Serum SERS Analysis: Example of Non‐Communicable Diseases Discrimination DOI Open Access
Lyudmila A. Bratchenko, Yulia А. Khristoforova,

Irina A. Pimenova

et al.

Journal of Biophotonics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 6, 2025

ABSTRACT The aim of the study is to compare performance surface‐enhanced Raman spectroscopy (SERS) analysis serum using a non‐cooled detector ( EnSpectr R785 ) and high spectral resolution Renishaw in task discrimination between patients with chronic heart failure obstructive pulmonary disease. SERS‐based solution classification problem demonstrates an insignificant relationship disease accuracy quality (classification for high‐resolution setup 0.84 low‐cost 0.81). In data recorded on setup, most significant bands are 611, 675, 720, 804, 1187, 1495, 1847 cm −1 ; setup—721, 1051, 1665 . results have revealed equal capabilities setups; however, has more prospects identifying contribution pathologically associated analytes.

Language: Английский

Citations

1
Comment on “Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning” DOI Creative Commons
Ivan А. Bratchenko, Lyudmila A. Bratchenko

Light Science & Applications, Journal Year: 2025, Volume and Issue: 14(1)

Published: Jan. 20, 2025

In their recent publication, Dong et al. 1 demonstrated high performance of SERS-based liquid biopsy human serum analysis for earlier cancer detection.Moreover, this study was highlighted in News & Vies section Light Science and Applications journal by Shi 2 .No doubt, different optical techniques nowadays are widely used the biofluids 3-10 demonstrate accuracy possible clinical applications.At same time, paper may be treated incorrectly due to drawbacks spectral data analysis.First all, proposed evaluate provided classification models based on separation acquired into training test sets.This is an adequate approach machine learning prove stability 11 .In it required model both set (comparability proves stability).Dong divided 4:1 ratio (training:test), but surprisingly only one value characterize models.Actually mixed from groups such value.It clearly seen data, presented figures text, that demonstrates whole dataset instead two values test.As example collected spectra 244 lung (LC) patients 324 healthy controls (HC).

Language: Английский

Citations

0
Overestimation of the classification model for Raman spectroscopy data of biological samples DOI Creative Commons
Ivan А. Bratchenko, Lyudmila A. Bratchenko

mSystems, Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

Language: Английский

Citations

0
Raman liquid biopsy: a new approach to the multiple sclerosis diagnostics DOI Creative Commons

Anna V. Neupokoeva,

Ivan А. Bratchenko, Lyudmila A. Bratchenko

et al.

Frontiers in Neurology, Journal Year: 2025, Volume and Issue: 16

Published: April 16, 2025

Despite the prevalence of multiple sclerosis, there is currently no biomarker by which this disease can be reliably identified. Existing diagnostic methods are either expensive or have low specificity. Therefore, search for a method with high specificity and sensitivity, at same time not requiring complex sample processing equipment, urgent. The article discusses use blood serum surface enhanced Raman spectroscopy in combination machine learning analysis to separate persons sclerosis healthy individuals. As spectra projection on latent structures-discriminant was used. Using above methods, we obtained possibility ones an average 0.96 sensitivity 0.89. main bands discrimination against individuals 632, 721-735, 1,048-1,076 cm-1. In general, study spectral properties using promising diagnosing however, further detailed studies area required.

Language: Английский

Citations

0
The surface-enhanced Raman scattering method for point-of-care atrial fibrillation diagnostics DOI
I. A. Boginskaya, Robert R. Safiullin, Victoria Tikhomirova

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 189, P. 109923 - 109923

Published: March 4, 2025

Language: Английский

Citations

0
Detection of Respiratory Disease Based on Surface-Enhanced Raman Scattering and Multivariate Analysis of Human Serum DOI Creative Commons
Yulia А. Khristoforova, Lyudmila A. Bratchenko, В. И. Купаев

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 660 - 660

Published: March 8, 2025

Background/Objectives: Chronic obstructive pulmonary disease (COPD) is a significant public health concern, affecting millions of people worldwide. This study aims to use Surface-Enhanced Raman Scattering (SERS) technology detect the presence respiratory conditions, with focus on COPD. Methods: The samples human serum from 41 patients diseases (11 COPD, 20 bronchial asthma (BA), and 10 asthma–COPD overlap syndrome) 103 ischemic heart disease, complicated by chronic failure (CHF), were analyzed using SERS. A multivariate analysis SERS characteristics was performed Partial Least Squares Discriminant Analysis (PLS-DA) classify following groups: (1) all versus pathological referent group, which included CHF patients, (2) COPD those BA. Results: We found that combination at 638 1051 cm−1 could help identify diseases. PLS-DA model achieved mean predictive accuracy 0.92 for classifying group (0.85 sensitivity, 0.97 specificity). However, in case differentiating between BA, only 0.61. Conclusions: Therefore, metabolic proteomic composition shows differences compared but BA are less significant, suggesting similarity general pathogenetic mechanisms these two conditions.

Language: Английский

Citations

0
SERS-based technique for accessible and rapid diagnosis of multiple myeloma in blood serum analysis DOI
Lyudmila A. Bratchenko, Yulia А. Khristoforova,

Irina A. Pimenova

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 6(0), P. 1 - 1

Published: Jan. 1, 2025

Language: Английский

Citations

0
Detection of Chronic Obstructive Pulmonary Disease Based on Sers and Multivariate Analysis of Human Serum DOI
Yulia А. Khristoforova, Lyudmila A. Bratchenko,

Vitaly Kupaev

et al.

Published: Jan. 1, 2024

Chronic obstructive pulmonary disease (COPD) is a significant public health disease, affecting millions of subjects globally. This study proposes Surface-Enhanced Raman Scattering (SERS) technique to determine the presence respiratory diseases and particularly COPD. The samples human serum 41 patients with diseases, 103 chronic heart (CHD), 25 healthy control were analyzed by means SERS. Multivariate analysis SERS characteristics was performed Partial Least Squares Discriminant Analysis (PLS-DA) classify (1) all versus group including CHD (2) COPD bronchial asthma (BA). We found that combination at 638 1051 cm−1 can help identifying diseases. PLS-DA model achieved mean predictive accuracy 0.92 for classifying comparable controls (0.88 sensitivity, 0.96 specificity). However, in cases differentiation BA only 0.61. Therefore, metabolic proteomic composition has differences compared cases, but between are less significant, indicating similarity general pathogenetic mechanisms these two conditions.

Language: Английский

Citations

0
Comment on “utilizing Raman spectroscopy for urinalysis to diagnose acute kidney injury stages in cardiac surgery patients” DOI Creative Commons
Ivan А. Bratchenko, Lyudmila A. Bratchenko

Renal Failure, Journal Year: 2024, Volume and Issue: 46(2)

Published: Dec. 3, 2024

Language: Английский

Citations

0