dc.contributor.author | Panteli, Christoforos | en |
dc.contributor.author | Stylianou, Marios | en |
dc.contributor.author | Anastasiou, Andreas | en |
dc.contributor.author | Andreou, Chrysafis | en |
dc.contributor.editor | Vig, John | en |
dc.contributor.editor | Carrara, Sandro | en |
dc.contributor.editor | Shkel, Andrei | en |
dc.coverage.spatial | Vienna | en |
dc.creator | Panteli, Christoforos | en |
dc.creator | Stylianou, Marios | en |
dc.creator | Anastasiou, Andreas | en |
dc.creator | Andreou, Chrysafis | en |
dc.date.accessioned | 2024-01-15T08:28:49Z | |
dc.date.available | 2024-01-15T08:28:49Z | |
dc.date.issued | 2023-11-28 | |
dc.identifier.isbn | 979-8-3503-0387-2 | |
dc.identifier.issn | 2168-9229 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/65987 | en |
dc.description.abstract | Quick identification of bacterial growth in culture is demonstrated using time series analysis of gas phase data collected by a commercial gas sensor. Clinically standard blood agar plates were inoculated with different concentrations of Escherichia coli and incubated at 37 C. BME688 gas sensors and read-out electronics were used to measure the gases emitted during bacterial growth. The time series show sigmoidal features, which appear at earlier time points with higher initial concen- trations. A modified logistic model was found to fit the data well and confirms faster growth with higher concentrations. A data analysis algorithm was developed to identify the earliest time- point for bacterial growth detection; it revealed a logarithmic relationship between the initial seeding concentration and time for signal peaks. Concentration-dependent bacterial growth was identified at 2-6 hours, more than 90% faster detection, compared to the current clinical protocols that take up to 72 hours. | en |
dc.description.sponsorship | Horizon Europe Marie Sklodowska Curie Actions research fellowship (project) | en |
dc.language.iso | eng | en |
dc.publisher | IEEE | en |
dc.relation | HORIZON-MSCA-2021-PF-01 / 101062837 | en |
dc.rights | CC0 1.0 Universal | * |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | * |
dc.source | IEEE Sensors 2023 Conference Proceedings | en |
dc.source.uri | https://ieeexplore.ieee.org/abstract/document/10324881 | en |
dc.subject | Commercial gas sensor | en |
dc.subject | in-vitro bacteria infection | en |
dc.subject | gas-phase analysis | en |
dc.subject | UTI | en |
dc.title | Rapid detection of bacterial infection using gas phase time series analysis | en |
dc.type | info:eu-repo/semantics/article | en |
dc.identifier.doi | 10.1109/SENSORS56945.2023.10324881 | |
dc.author.faculty | 007 Πολυτεχνική Σχολή / Faculty of Engineering | |
dc.author.department | Τμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών / Department of Electrical and Computer Engineering | |
dc.type.uhtype | Article | en |
dc.rights.embargodate | 2024-03-29 | |
dc.contributor.orcid | Panteli, Christoforos [0000-0002-6554-7192] | |
dc.contributor.orcid | Andreou, Chrysafis [0000-0002-3464-9110] | |
dc.type.subtype | CONFERENCE_PROCEEDINGS | en |
dc.gnosis.orcid | 0000-0002-6554-7192 | |
dc.gnosis.orcid | 0000-0002-3464-9110 | |