Automating data classification for label-free point-of-care biosensing in real complex samples

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F24%3A00599585" target="_blank" >RIV/68378271:_____/24:00599585 - isvavai.cz</a>

Výsledek na webu

<a href="https://hdl.handle.net/11104/0357035" target="_blank" >https://hdl.handle.net/11104/0357035</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.sna.2024.115501" target="_blank" >10.1016/j.sna.2024.115501</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automating data classification for label-free point-of-care biosensing in real complex samples

Popis výsledku v původním jazyce

Surface-based affinity biosensors present a promising avenue for point-of-care (POC) pathogen detection in real-world samples. This paper introduces a procedure for automatically classifying pathogen presence in unprocessed liquids from direct detection data measured by a simple POC quartz crystal microbalance sensor device. We show that the developed procedure exhibits exceptional robustness across different biosensing assays and complex real-world media. Through optimizing parameters using diverse datasets encompassing Escherichia coli O157:H7 (E. coli) and SARS-CoV-2 detection in various media, we achieved rates of successful detection as high as 80.8 % and 90.9 % for E. coli and SARS-CoV-2, respectively, without extensive machine learning. Our results suggest that this exceptionally robust method holds potential as a straightforward tool for automating sample classification in point-of-care diagnostics, underpinning its promising broader applicability.

Název v anglickém jazyce

Automating data classification for label-free point-of-care biosensing in real complex samples

Popis výsledku anglicky

Surface-based affinity biosensors present a promising avenue for point-of-care (POC) pathogen detection in real-world samples. This paper introduces a procedure for automatically classifying pathogen presence in unprocessed liquids from direct detection data measured by a simple POC quartz crystal microbalance sensor device. We show that the developed procedure exhibits exceptional robustness across different biosensing assays and complex real-world media. Through optimizing parameters using diverse datasets encompassing Escherichia coli O157:H7 (E. coli) and SARS-CoV-2 detection in various media, we achieved rates of successful detection as high as 80.8 % and 90.9 % for E. coli and SARS-CoV-2, respectively, without extensive machine learning. Our results suggest that this exceptionally robust method holds potential as a straightforward tool for automating sample classification in point-of-care diagnostics, underpinning its promising broader applicability.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10610 - Biophysics

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)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Sensors and Actuators A - Physical

ISSN

0924-4247

e-ISSN

1873-3069

Svazek periodika

374

Číslo periodika v rámci svazku

Aug

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

115501

Kód UT WoS článku

001246546400001

EID výsledku v databázi Scopus

2-s2.0-85194105928