@article{KUNCAK2024115501,

title = {Automating data classification for label-free point-of-care biosensing in real complex samples},

author = {Jakub Kunčák and Michala Forinová and Alina Pilipenco and Viktor Procházka and Petr Horák and Sofya Dmitrievna Sycheva and Ivan Gennadievich Deyneka and Hana Vaisocherová-Lísalová},

url = {https://www.sciencedirect.com/science/article/pii/S0924424724004953},

doi = {https://doi.org/10.1016/j.sna.2024.115501},

issn = {0924-4247},

year  = {2024},

date = {2024-05-19},

urldate = {2024-01-01},

journal = {Sensors and Actuators A: Physical},

pages = {115501},

abstract = {Surface-based affinity biosensors present a promising avenue for point-of-care (POC) pathogen detection in real-world samples. While laboratory-based devices commonly employ various techniques to mitigate noise, signal drifts, fluidic artifacts, and other system imperfections, their simple cost-effective POC counterparts designed for field use frequently lack such capabilities. This paper addresses this gap by introducing a procedure for automatically classifying pathogen presence in unprocessed liquids from direct detection data measured by a simple POC quartz crystal microbalance sensor device. The procedure integrates classical analytical tools such as filtering, data selection, baseline de-drifting, and result calculation in tailored successive steps, considering the nature of the sensor signal and the challenges posed by real-world media. 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 including food-derived matrices and cell culture 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. Furthermore, we analyse the sensitivity of the procedure to variations of input parameters and with examples discuss key factors influencing overall procedure accuracy. 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.},

keywords = {. O157:H7, automatic data classification, Detection of pathogens, openQCM Q-1, Point-of-care biosensors, QCM-D, Quartz Crystal Microbalance, SARS-CoV-2},

pubstate = {published},

tppubtype = {article}

}

@article{kunvcak2024automating,

title = {Automating Data Analysis for Point-of-Care Label-Free Surface-Based Affinity Biosensors Dealing with Complex Biological Samples: Escherichia Coli O157: H7 and Sars-Cov-2 Case Studies},

author = {Jakub Kunčák and Michala Forinová and Alina Pilipenco and Viktor Procházka and Petr Horák and Sycheva Sofya Dmitrievna and Ivan Gennadievich Deyneka and Hana Vaisocherová-L'isalová},

url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4756321},

year  = {2024},

date = {2024-03-20},

urldate = {2024-03-20},

journal = {Available at SSRN 4756321},

abstract = {Surface-based affinity biosensors offer a promising avenue for point-of-care (POC) detection of pathogens in real-world samples. While laboratory-based devices commonly employ various techniques to compensate for noise, signal drifts, fluidic artifacts, and other system imperfections, their POC counterparts aiming at providing simple cost-effective detection platforms for field use, often lack these qualities. This paper addresses this gap by introducing a procedure for automatic classification of pathogen presence in unprocessed liquids from direct detection data measured by a simple POC-relevant quartz crystal microbalance sensor device.  By considering the nature of the sensor signal and the sources of its imperfections in real-world media, a straightforward procedure integrates “classical” analytical tools (filtering, data selection, baseline de-drifting, and result calculation) in successive steps to automate sample classification without the need for extensive machine learning. Through optimizing parameters using diverse datasets encompassing Escherichia coli O157:H7 (E. coli) and SARS-CoV-2 detection in various media including food-derived matrices and cell culture media, we achieved rates of successful detection as high as 80.8% and 90.9% for E. coli and SARS-CoV-2, respectively. Furthermore, we analyse the sensitivity of the routine to variations of input parameters and with examples discuss the key factors influencing the accuracy of the overall procedure. The results show that the developed method exhibits exceptional robustness across different biosensing assays and complex real-world media, highlighting its promising broader applicability in point-of-care diagnostics.},

keywords = {automatic data classification, Detection of pathogens, E. coli O157:H7, openQCM, openQCM Q-1, Point-of-care biosensors, Quartz Crystal Microbalance, SARS-CoV-2},

pubstate = {published},

tppubtype = {article}

}