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Detection of Polysaccharide Markers of Fungal Infections by Surface-Enhanced Raman Scattering and Machine Learning Methods Научная публикация

Журнал Biosensors-Basel
ISSN: 2079-6374
Вых. Данные Год: 2026, Том: 16, Номер: 9, Номер статьи : 502, Страниц : DOI: 10.3390/bios16090502
Авторы Zvyagina Julia Yu. 1 , Safiullin Robert R. 1,2 , Naboko Andrey S. 1 , Polozov Victor I. 1 , Boginskaya Irina A. 1 , Sedova Marina V. 1 , Krylov Vadim B. 3 , Yashunsky Dmitry V. 3 , Argunov Dmitry A. 3 , Nifantiev Nikolay E. 3 , Ryzhikov Ilya A. 4,1 , Merzlikin Alexander M. 1 , Lagarkov Andrey N. 1
Организации
1 Institute for Theoretical and Applied Electromagnetics RAS, 125412 Moscow, Russia
2 Moscow Center for Advanced Studies, 123592 Moscow, Russia
3 N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, 119991 Moscow, Russia
4 FMN Laboratory, Bauman Moscow State Technical University, 105005 Moscow, Russia

Реферат: In this study, we used the SERS method for the first time to measure the spectra of four polysaccharide markers of fungal infections: linear β-(1→3)- and β-(1→6)-linked D-glucans, branched mannan of Candida albicans and galactomannan of Aspergillus fumigatus. Aqueous solutions of the polysaccharides were studied in concentrations from 10 pg/mL to 100 μg/mL. The spectra were analyzed using machine learning methods: principal component analysis for data visualization and partial least squares with a ridge regularizer, which were used to construct metrics reflecting the accuracy of substance recognition relative to each other. The spectral changes with varying analyte concentration were observed and stable calibration has been achieved. Subsequent measurements of fungal polysaccharides in the presence of a physiological concentration of human serum albumin (45 mg/mL), used to model blood serum, enabled accurate analyte detection in a clinically relevant concentration range of 10 pg/mL to 100 ng/mL. In this case, the calibration dependence was calculated using the partial least squares method with the L1-regularizer. Blind testing was evaluated using a train-derived applicability-domain criterion based on the disagreement between the model prediction and an independent concentration estimate.
Библиографическая ссылка: Zvyagina J.Y. , Safiullin R.R. , Naboko A.S. , Polozov V.I. , Boginskaya I.A. , Sedova M.V. , Krylov V.B. , Yashunsky D.V. , Argunov D.A. , Nifantiev N.E. , Ryzhikov I.A. , Merzlikin A.M. , Lagarkov A.N.
Detection of Polysaccharide Markers of Fungal Infections by Surface-Enhanced Raman Scattering and Machine Learning Methods
Biosensors-Basel. 2026. V.16. N9. 502 . DOI: 10.3390/bios16090502 WOS Scopus OpenAlex
Даты:
Поступила в редакцию: 28 июл. 2026 г.
Опубликована online: 8 сент. 2026 г.
Идентификаторы БД:
≡ Web of science: WOS:001890303200001
≡ Scopus: 2-s2.0-105051527441
≡ OpenAlex: W7211966982
Альметрики: