Precise cancer detection via the combination of functionalized SERS surfaces and convolutional neural network with independent inputs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22310%2F20%3A43921087" target="_blank" >RIV/60461373:22310/20:43921087 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60461373:22330/20:43921087 RIV/68407700:21230/20:00338035 RIV/00216208:11110/20:10411409

Výsledek na webu

<a href="https://doi.org/10.1016/j.snb.2020.127660" target="_blank" >https://doi.org/10.1016/j.snb.2020.127660</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.snb.2020.127660" target="_blank" >10.1016/j.snb.2020.127660</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Precise cancer detection via the combination of functionalized SERS surfaces and convolutional neural network with independent inputs

Popis výsledku v původním jazyce

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&apos;s metabolites were classified by the trained CNN and perfectly distinguished with 100 % prediction accuracy. © 2020 Elsevier B.V.

Název v anglickém jazyce

Precise cancer detection via the combination of functionalized SERS surfaces and convolutional neural network with independent inputs

Popis výsledku anglicky

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&apos;s metabolites were classified by the trained CNN and perfectly distinguished with 100 % prediction accuracy. © 2020 Elsevier B.V.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20501 - Materials engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Sensors and Actuators B

ISSN

0925-4005

e-ISSN

—

Svazek periodika

308

Číslo periodika v rámci svazku

APR 1 2020

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

"127660/1"-9

Kód UT WoS článku

000511146700041

EID výsledku v databázi Scopus

2-s2.0-85077692131