Precise cancer detection via the combination of functionalized SERS surfaces and convolutional neural network with independent inputs; Sensors and Actuators B: Chemical; Vol. 308

Precise cancer detection via the combination of functionalized SERS surfaces and convolutional neural network with independent inputs; Sensors and Actuators B: Chemical; Vol. 308

Bibliografske podrobnosti
Parent link:Sensors and Actuators B: Chemical
Vol. 308.— 2020.— [127660, 9 p.]
Corporate Authors: Национальный исследовательский Томский политехнический университет Исследовательская школа химических и биомедицинских технологий, Национальный исследовательский Томский политехнический университет Исследовательская школа физики высокоэнергетических процессов
Drugi avtorji: Erzina M. R. Mariya Rashidovna, Trelin A. Andrey, Guselnikova O. A. Olga Andreevna, Dvorankova B. Barbara, Strnadova K., Perminova A. Anastasiya, Ulbrikh P., Maresh D., Zherabek V., Elashnikov R. Roman, Svorcik V. Vaclav, Lyutakov O. Oleksy
Izvleček:Title screen
Combining the advanced approaches of surface functionalization and chemistry, plasmonics, surface enhanced Raman spectroscopy (SERS), and machine learning, we propose the advanced route for express and precise recognition of normal and cancer cells. Our interdisciplinary approach uses plasmonic coupling between the specific nanoparticles and underlying periodical plasmonic surface and achieves high SERS enhancement factor. The surface of gold multibranched nanoparticles (AuMs) was functionalized with different chemical groups to achieve partially selective entrapping of biomolecules from cells cultivation media and generate information-rich inputs for machine learning methods and SERS-based cells recognition. Evaluation of convolutional neural networks (CNN) training results, performed with ad hoc feature selection method, suggests that the grafted functional groups provide specificity to proteins, nucleic acids and lipids, responsible for cancer line identification. The dataset of SERS control spectra of normal and cancer cell’s metabolites were classified by the trained CNN and perfectly distinguished with 100 % prediction accuracy.
Режим доступа: по договору с организацией-держателем ресурса
Jezik:angleščina
Izdano: 2020
Teme:
труды учёных ТПУ
электронный ресурс
cancer detection
SERS
surface functionalization
convolutional neural network
Online dostop:https://doi.org/10.1016/j.snb.2020.127660
Format: Elektronski Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=663798

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