Modelling and prediction of bacterial attachment to polymers
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 develo...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
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Wiley
2014
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| Online Access: | https://eprints.nottingham.ac.uk/30873/ |
| _version_ | 1848794080760299520 |
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| author | 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. |
| author_facet | 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. |
| author_sort | Epa, V.C. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:10:31Z |
| format | Article |
| id | nottingham-30873 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:10:31Z |
| publishDate | 2014 |
| publisher | Wiley |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-308732020-05-04T16:46:52Z https://eprints.nottingham.ac.uk/30873/ 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. 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. Wiley 2014-04-09 Article PeerReviewed Epa, V.C., Hook, Andrew L., Chang, C., Yang, Jing, Langer, Robert, Anderson, Daniel G., Williams, P., Davies, Martyn C., 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 high throughput; structure–property relationship; pathogen attachment; sparse Bayesian methods; medical devices; nosocomial infections http://onlinelibrary.wiley.com/doi/10.1002/adfm.201302877/abstract doi:10.1002/adfm.201302877 doi:10.1002/adfm.201302877 |
| spellingShingle | high throughput; structure–property relationship; pathogen attachment; sparse Bayesian methods; medical devices; nosocomial infections 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. Modelling and prediction of bacterial attachment to polymers |
| title | Modelling and prediction of bacterial attachment to polymers |
| title_full | Modelling and prediction of bacterial attachment to polymers |
| title_fullStr | Modelling and prediction of bacterial attachment to polymers |
| title_full_unstemmed | Modelling and prediction of bacterial attachment to polymers |
| title_short | Modelling and prediction of bacterial attachment to polymers |
| title_sort | modelling and prediction of bacterial attachment to polymers |
| topic | high throughput; structure–property relationship; pathogen attachment; sparse Bayesian methods; medical devices; nosocomial infections |
| url | https://eprints.nottingham.ac.uk/30873/ https://eprints.nottingham.ac.uk/30873/ https://eprints.nottingham.ac.uk/30873/ |