SERS and advanced chemometrics - Utilization of Siamese neural network for picomolar identification of beta-lactam antibiotics resistance gene fragment

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22310%2F22%3A43925042" target="_blank" >RIV/60461373:22310/22:43925042 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60461373:22330/22:43925042 RIV/60461373:22340/22:43925042 RIV/00216208:11320/22:10452098

Výsledek na webu

<a href="https://doi.org/10.1016/j.aca.2021.339373" target="_blank" >https://doi.org/10.1016/j.aca.2021.339373</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.aca.2021.339373" target="_blank" >10.1016/j.aca.2021.339373</a>

Jazyk výsledku

angličtina

Název v původním jazyce

SERS and advanced chemometrics - Utilization of Siamese neural network for picomolar identification of beta-lactam antibiotics resistance gene fragment

Popis výsledku v původním jazyce

The enormous development and expansion of antibiotic-resistant bacterial strains impel the intensive search for new methods for fast and reliable detection of antibiotic susceptibility markers. Here, we combined DNA-targeted surface functionalization, surface-enhanced Raman spectroscopy (SERS) measurements, and subsequent spectra processing by decision system (DS) for detection of a specific oligonucleotide (ODN) sequence identical to a fragment of blaNDM-1 gene, responsible for beta-lactam antibiotic resistance. The SERS signal was measured on plasmonic gold grating, functionalized with capture ODN, ensuring the binding of corresponded ODNs. Designed DS consists of a Siamese neural network (SNN) coupled with robust statistics and Bayes decision theory. The proposed approach allows manipulation with complex multicomponent samples and predefine the desired detection level of confidence and errors, automatically determining the number of required spectra and samples. In constant to commonly used classification-type SNN, our method was applied to analyze samples with compositions previously &quot;unknown&quot; to DS. The detection of targeted ODN was performed with &gt;= 99% level of confidence up to 3 x 10(-12) M limit on the background of 10(-10) M concentration of similar but not targeted ODNs. (C) 2021 Elsevier B.V. All rights reserved.

Název v anglickém jazyce

SERS and advanced chemometrics - Utilization of Siamese neural network for picomolar identification of beta-lactam antibiotics resistance gene fragment

Popis výsledku anglicky

The enormous development and expansion of antibiotic-resistant bacterial strains impel the intensive search for new methods for fast and reliable detection of antibiotic susceptibility markers. Here, we combined DNA-targeted surface functionalization, surface-enhanced Raman spectroscopy (SERS) measurements, and subsequent spectra processing by decision system (DS) for detection of a specific oligonucleotide (ODN) sequence identical to a fragment of blaNDM-1 gene, responsible for beta-lactam antibiotic resistance. The SERS signal was measured on plasmonic gold grating, functionalized with capture ODN, ensuring the binding of corresponded ODNs. Designed DS consists of a Siamese neural network (SNN) coupled with robust statistics and Bayes decision theory. The proposed approach allows manipulation with complex multicomponent samples and predefine the desired detection level of confidence and errors, automatically determining the number of required spectra and samples. In constant to commonly used classification-type SNN, our method was applied to analyze samples with compositions previously &quot;unknown&quot; to DS. The detection of targeted ODN was performed with &gt;= 99% level of confidence up to 3 x 10(-12) M limit on the background of 10(-10) M concentration of similar but not targeted ODNs. (C) 2021 Elsevier B.V. All rights reserved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20501 - Materials engineering

Projekt

<a href="/cs/project/GA21-06065S" target="_blank" >GA21-06065S: Nové funkcionalizované senzory založené na plazmonech jako nástroje pro monitorování buněk a pro pokročilé tkáňové inženýrství</a><br>

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

Analytica Chimica Acta

ISSN

0003-2670

e-ISSN

—

Svazek periodika

1192

Číslo periodika v rámci svazku

FEB 1 2022

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

"339373/1"-9

Kód UT WoS článku

000735770400006

EID výsledku v databázi Scopus

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