Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices
Bacterial infections in healthcare settings are a frequent accompaniment to both routine procedures such as catheterization and surgical site interventions. Their impact is becoming even more marked as the numbers of medical devices that are used to manage chronic health conditions and improve quali...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
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American Chemical Society
2017
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| Online Access: | https://eprints.nottingham.ac.uk/48614/ |
| _version_ | 1848797807411986432 |
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| author | Mikulskis, Paulius Hook, Andrew Dundas, Adam Irvine, Derek J. Sanni, Olutoba Anderson, Daniel Langer, Robert Alexander, Morgan R. Williams, Paul Winkler, David A. |
| author_facet | Mikulskis, Paulius Hook, Andrew Dundas, Adam Irvine, Derek J. Sanni, Olutoba Anderson, Daniel Langer, Robert Alexander, Morgan R. Williams, Paul Winkler, David A. |
| author_sort | Mikulskis, Paulius |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Bacterial infections in healthcare settings are a frequent accompaniment to both routine procedures such as catheterization and surgical site interventions. Their impact is becoming even more marked as the numbers of medical devices that are used to manage chronic health conditions and improve quality of life increases. The resistance of pathogens to multiple antibiotics is also increasing, adding an additional layer of complexity to the problems of employing safe and effective medical procedures. One approach to reducing the rate of infections associated with implanted and indwelling medical devices is the use of polymers that resist the formation of bacterial biofilms. To significantly accelerate the discovery of such materials, we show how state of the art machine learning methods can generate quantitative predictions for the attachment of multiple pathogens to a large library of polymers in a single model for the first time. Such models facilitate design of polymers with very low pathogen attachment across different bacterial species that will be candidate materials for implantable or indwelling medical devices such as urinary catheters, cochlear implants and pacemakers. |
| first_indexed | 2025-11-14T20:09:45Z |
| format | Article |
| id | nottingham-48614 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:09:45Z |
| publishDate | 2017 |
| publisher | American Chemical Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-486142020-05-04T19:20:41Z https://eprints.nottingham.ac.uk/48614/ Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices Mikulskis, Paulius Hook, Andrew Dundas, Adam Irvine, Derek J. Sanni, Olutoba Anderson, Daniel Langer, Robert Alexander, Morgan R. Williams, Paul Winkler, David A. Bacterial infections in healthcare settings are a frequent accompaniment to both routine procedures such as catheterization and surgical site interventions. Their impact is becoming even more marked as the numbers of medical devices that are used to manage chronic health conditions and improve quality of life increases. The resistance of pathogens to multiple antibiotics is also increasing, adding an additional layer of complexity to the problems of employing safe and effective medical procedures. One approach to reducing the rate of infections associated with implanted and indwelling medical devices is the use of polymers that resist the formation of bacterial biofilms. To significantly accelerate the discovery of such materials, we show how state of the art machine learning methods can generate quantitative predictions for the attachment of multiple pathogens to a large library of polymers in a single model for the first time. Such models facilitate design of polymers with very low pathogen attachment across different bacterial species that will be candidate materials for implantable or indwelling medical devices such as urinary catheters, cochlear implants and pacemakers. American Chemical Society 2017-12-01 Article PeerReviewed Mikulskis, Paulius, Hook, Andrew, Dundas, Adam, Irvine, Derek J., Sanni, Olutoba, Anderson, Daniel, Langer, Robert, Alexander, Morgan R., Williams, Paul and Winkler, David A. (2017) Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices. ACS Applied Materials and Interfaces, 10 (1). pp. 139-149. ISSN 1944-8252 medical devices; broad spectrum; antimicrobial surfaces; machine learning; polymer arrays http://pubs.acs.org/doi/10.1021/acsami.7b14197 doi:10.1021/acsami.7b14197 doi:10.1021/acsami.7b14197 |
| spellingShingle | medical devices; broad spectrum; antimicrobial surfaces; machine learning; polymer arrays Mikulskis, Paulius Hook, Andrew Dundas, Adam Irvine, Derek J. Sanni, Olutoba Anderson, Daniel Langer, Robert Alexander, Morgan R. Williams, Paul Winkler, David A. Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices |
| title | Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices |
| title_full | Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices |
| title_fullStr | Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices |
| title_full_unstemmed | Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices |
| title_short | Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices |
| title_sort | prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices |
| topic | medical devices; broad spectrum; antimicrobial surfaces; machine learning; polymer arrays |
| url | https://eprints.nottingham.ac.uk/48614/ https://eprints.nottingham.ac.uk/48614/ https://eprints.nottingham.ac.uk/48614/ |