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

Result code in 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>

Alternative codes found

RIV/00216208:11110/22:10445140

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20501 - Materials engineering

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

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

Publication year

2022

Confidentiality

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

Name of the periodical

Sensors and Actuators B

ISSN

0925-4005

e-ISSN

—

Volume of the periodical

367

Issue of the periodical within the volume

SEP 15 2022

Country of publishing house

CH - SWITZERLAND

Number of pages

8

Pages from-to

"132057/1"-8

UT code for WoS article

000807805200004

EID of the result in the Scopus database

2-s2.0-85130582189