Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22310%2F22%3A43924620" target="_blank" >RIV/60461373:22310/22:43924620 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11110/22:10445140

Výsledek na webu

<a href="https://doi.org/10.1016/j.snb.2022.132057" target="_blank" >https://doi.org/10.1016/j.snb.2022.132057</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.snb.2022.132057" target="_blank" >10.1016/j.snb.2022.132057</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach

Popis výsledku v původním jazyce

Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive tool in medical diagnostics and bioanalysis fields, aimed at the qualitative detection of relevant biomolecules. However, quantitative SERS analysis of complex (bio)samples is a more challenging and, in many cases, almost impossible task, requiring functional SERS substrates or advanced spectral data analysis. In this work, we propose the combination of a functional SERS substrate, capable of trapping target biomolecules, with CNN transfer learning for quantitative detection of the relevant α1-acid glycoprotein (AGP, also known as orosomucoid) in human serum. As a SERS substrate, the plasmonic gold grating was functionalized with boronic acid moieties to entrap target AGP. The functionality of the substrate was tested on two model solutions: a solution containing saccharides as competing molecules and human serum with added AGP, which is close to real samples. The convolution neural network (CNN) was previously trained on a huge number of (bio)samples. Then CNN transfer learning was used to quantify AGP concentration in model samples, as well as in human serum. Developed strategy is able to identify the alarming increase of AGP concentration in an express and medically decentralized way, on short time and under lack of spectral data. Generally, the proposed combination of SERS and machine transfer learning could be expanded to a range of alternative cases, where the collection of real samples is restricted and can be substituted by the measurements of similar model systems, without loss of analysis reliability. © 2022 Elsevier B.V.

Název v anglickém jazyce

Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach

Popis výsledku anglicky

Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive tool in medical diagnostics and bioanalysis fields, aimed at the qualitative detection of relevant biomolecules. However, quantitative SERS analysis of complex (bio)samples is a more challenging and, in many cases, almost impossible task, requiring functional SERS substrates or advanced spectral data analysis. In this work, we propose the combination of a functional SERS substrate, capable of trapping target biomolecules, with CNN transfer learning for quantitative detection of the relevant α1-acid glycoprotein (AGP, also known as orosomucoid) in human serum. As a SERS substrate, the plasmonic gold grating was functionalized with boronic acid moieties to entrap target AGP. The functionality of the substrate was tested on two model solutions: a solution containing saccharides as competing molecules and human serum with added AGP, which is close to real samples. The convolution neural network (CNN) was previously trained on a huge number of (bio)samples. Then CNN transfer learning was used to quantify AGP concentration in model samples, as well as in human serum. Developed strategy is able to identify the alarming increase of AGP concentration in an express and medically decentralized way, on short time and under lack of spectral data. Generally, the proposed combination of SERS and machine transfer learning could be expanded to a range of alternative cases, where the collection of real samples is restricted and can be substituted by the measurements of similar model systems, without loss of analysis reliability. © 2022 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

2022

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

367

Číslo periodika v rámci svazku

SEP 15 2022

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

8

Strana od-do

"132057/1"-8

Kód UT WoS článku

000807805200004

EID výsledku v databázi Scopus

2-s2.0-85130582189