Modelling and prediction of bacterial attachment to polymers

Epa, V.C. and Hook, Andrew L. and Chang, C. and Yang, Jing and Langer, Robert and Anderson, Daniel G. and Williams, P. and Davies, Martyn C. and Alexander, Morgan R. and Winkler, David A. (2014) Modelling and prediction of bacterial attachment to polymers. Advanced Functional Materials, 24 (14). pp. 2085-2093. ISSN 1616-3028

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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.

Item Type: Article
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.wiley.com/doi/10.1002/adfm.201302877/full. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Keywords: high throughput; structure–property relationship; pathogen attachment; sparse Bayesian methods; medical devices; nosocomial infections
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Pharmacy
Identification Number: https://doi.org/10.1002/adfm.201302877
Depositing User: Alexander, Morgan
Date Deposited: 20 Nov 2015 13:57
Last Modified: 14 Sep 2016 12:14
URI: http://eprints.nottingham.ac.uk/id/eprint/30873

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