Skip to main content

Research Repository

Advanced Search

Exploring the Relationship between Polymer Surface Chemistry and Bacterial Attachment Using ToF-SIMS and Self-Organizing maps

Wong, See Yoong; Hook, Andrew L.; Gardner, Wil; Chang, Chien‐Yi; Mei, Ying; Davies, Martyn C.; Williams, Paul; Alexander, Morgan R.; Ballabio, Davide; Muir, Benjamin W.; Winkler, David A.; Pigram, Paul J.

Exploring the Relationship between Polymer Surface Chemistry and Bacterial Attachment Using ToF-SIMS and Self-Organizing maps Thumbnail


Authors

See Yoong Wong

ANDREW HOOK ANDREW.HOOK@NOTTINGHAM.AC.UK
Assistant Professor

Wil Gardner

Chien‐Yi Chang

Ying Mei

Martyn C. Davies

PAUL WILLIAMS PAUL.WILLIAMS@NOTTINGHAM.AC.UK
Professor of Molecular Microbiology

Profile Image

MORGAN ALEXANDER MORGAN.ALEXANDER@NOTTINGHAM.AC.UK
Professor of Biomedical Surfaces

Davide Ballabio

Benjamin W. Muir

David A. Winkler

Paul J. Pigram



Abstract

Biofilm formation is a major cause of hospital-acquired infections. Research into biofilm-resistant materials is therefore critical to reduce the frequency of these events. Polymer microarrays offer a high-throughput approach to enable the efficient discovery of novel biofilm-resistant polymers. Herein, bacterial attachment and surface chemistry are studied for a polymer microarray to improve the understanding of Pseudomonas aeruginosa biofilm formation on a diverse set of polymeric surfaces. The relationships between time-of-flight secondary ion mass spectrometry (ToF-SIMS) data and biofilm formation are analyzed using linear multivariate analysis (partial least squares [PLS] regression) and a nonlinear self-organizing map (SOM). The SOM models revealed several combinations of fragment ions that are positively or negatively associated with bacterial biofilm formation, which are not identified by PLS. With these insights, a second PLS model is calculated, in which interactions between key fragments (identified by the SOM) are explicitly considered. Inclusion of these terms improved the PLS model performance and shows that, without such terms, certain key fragment ions correlated with bacterial attachment may not be identified. The chemical insights provided by the combination of PLS regression and SOM will be useful for the design of materials that support negligible pathogen attachment.

Journal Article Type Article
Acceptance Date Jan 9, 2023
Online Publication Date Feb 15, 2023
Publication Date Mar 24, 2023
Deposit Date Apr 26, 2023
Publicly Available Date Apr 27, 2023
Journal Advanced Materials Interfaces
Electronic ISSN 2196-7350
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 10
Issue 9
Article Number 2202334
DOI https://doi.org/10.1002/admi.202202334
Keywords Artificial neural networks; bacterial attachment; mass segmentation; microarrays; polymers; self-organizing maps; ToF-SIMS
Public URL https://nottingham-repository.worktribe.com/output/17386129
Publisher URL https://onlinelibrary.wiley.com/doi/10.1002/admi.202202334

Files





You might also like



Downloadable Citations