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Modelling and Prediction of Bacterial Attachment to Polymers

Epa, V.C.; Hook, Andrew L.; Chang, C.; Yang, Jing; Langer, Robert; Anderson, Daniel G.; Williams, P.; Davies, Martyn C.; Alexander, Morgan R.; Winkler, David A.

Authors

V.C. Epa

ANDREW HOOK ANDREW.HOOK@NOTTINGHAM.AC.UK
Nottingham Research Fellow

C. Chang

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JING YANG JING.YANG@NOTTINGHAM.AC.UK
Assistant Professor

Robert Langer

Daniel G. Anderson

PAUL WILLIAMS paul.williams@nottingham.ac.uk
Professor of Molecular Microbiology

Martyn C. Davies

David A. Winkler



Abstract

Infection by pathogenic bacteria on implanted and indwelling medical devices during surgery causes large morbidity and mortality worldwide. Attempts to ameliorate this important medical issue have included development of antimicrobial surfaces on materials, ‘no touch’ surgical procedures, and development of materials with inherent low pathogen attachment. The search for new materials is increasingly being carried out by high throughput methods. Efficient methods for extracting knowledge from these large data sets are essential. We used data from a large polymer microarray exposed to three clinical pathogens to derive robust and predictive machine-learning models of pathogen attachment. The models could predict pathogen attachment for the polymer library quantitatively. The models also successfully predicted pathogen attachment for a second-generation library, and identified polymer surface chemistries that enhance or diminish pathogen attachment.

Citation

Epa, V., Hook, A. L., Chang, C., Yang, J., Langer, R., Anderson, D. G., …Winkler, D. A. (2014). Modelling and Prediction of Bacterial Attachment to Polymers. Advanced Functional Materials, 24(14), 2085-2093. https://doi.org/10.1002/adfm.201302877

Journal Article Type Article
Online Publication Date Dec 4, 2013
Publication Date Apr 9, 2014
Deposit Date Nov 20, 2015
Publicly Available Date Nov 20, 2015
Journal Advanced Functional Materials
Print ISSN 1616-301X
Electronic ISSN 1616-3028
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 24
Issue 14
Pages 2085-2093
DOI https://doi.org/10.1002/adfm.201302877
Keywords high throughput; structure–property relationship; pathogen attachment; sparse Bayesian methods; medical devices; nosocomial infections
Public URL http://eprints.nottingham.ac.uk/id/eprint/30873
Publisher URL https://onlinelibrary.wiley.com/doi/abs/10.1002/adfm.201302877
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information This is the pre-peer reviewed version of the following article: Epa, V. C., Hook, A. L., Chang, C., Yang, J., Langer, R., Anderson, D. G., Williams, P., Davies, M. C., Alexander, M. R. and Winkler, D. A. (2014), Modelling and Prediction of Bacterial Attachment to Polymers. Advanced Functional Materials, 24: 2085-2093. doi: 10.1002/adfm.201302877 which has been published in final form at http://onlinelibrary.wi...02/adfm.201302877/full. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.

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Epa et al AdvFunctMats 2014.pdf (858 Kb)
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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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