SERS and advanced chemometrics - Utilization of Siamese neural network for picomolar identification of beta-lactam antibiotics resistance gene fragment
The result's identifiers
Result code in 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>
Alternative codes found
RIV/60461373:22330/22:43925042 RIV/60461373:22340/22:43925042 RIV/00216208:11320/22:10452098
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
SERS and advanced chemometrics - Utilization of Siamese neural network for picomolar identification of beta-lactam antibiotics resistance gene fragment
Original language description
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 "unknown" to DS. The detection of targeted ODN was performed with >= 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - 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
Result continuities
Project
<a href="/en/project/GA21-06065S" target="_blank" >GA21-06065S: New functionalized plasmon-based sensors as tools for cell monitoring and advanced tissue engineering</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Analytica Chimica Acta
ISSN
0003-2670
e-ISSN
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Volume of the periodical
1192
Issue of the periodical within the volume
FEB 1 2022
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
9
Pages from-to
"339373/1"-9
UT code for WoS article
000735770400006
EID of the result in the Scopus database
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