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dc.contributor.authorEpa, V.C.*
dc.contributor.authorHook, A.L.*
dc.contributor.authorChang, Chien-Yi*
dc.contributor.authorYang, J.*
dc.contributor.authorLanger, R.*
dc.contributor.authorAnderson, D.G.*
dc.contributor.authorWilliams, P.*
dc.contributor.authorDavies, M.C.*
dc.contributor.authorAlexander, M.R.*
dc.contributor.authorWinkler, D.A.*
dc.date.accessioned2018-02-15T13:02:18Z
dc.date.available2018-02-15T13:02:18Z
dc.date.issued2014-04-09
dc.identifier.citationEpa VC, Hook AL, Chang C et al (2014) Modelling and prediction of bacterial attachment to polymers. Advanced Functional Materials. 24(14): 2085-2093.en_US
dc.identifier.urihttp://hdl.handle.net/10454/14961
dc.descriptionYesen_US
dc.description.abstractInfection 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. Data from a large polymer microarray exposed to three clinical pathogens is used to derive robust and predictive machine-learning models of pathogen attachment. The models can predict pathogen attachment for the polymer library quantitatively. The models also successfully predict pathogen attachment for a second-generation library, and identify polymer surface chemistries that enhance or diminish pathogen attachment.en_US
dc.description.sponsorshipCSIRO Advanced Materials Transformational Capability Platform. Newton Turner Award for Exceptional Senior Scientists. Wellcome Trust. Grant Number: 085245. NIH. Grant Number: R01 DE016516en_US
dc.language.isoenen_US
dc.rights© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim This is the peer reviewed version of the following article: Epa VC, Hook AL, Chang C et al (2014) Modelling and prediction of bacterial attachment to polymers. Advanced Functional Materials. 24(14): 2085-2093, which has been published in final form at http://dx.doi.org/10.1002/adfm.201302877. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.en_US
dc.subjectStaphylococcus aureus; Pseudomonas aeruginosa and uropathogenic Escherichia coli; High throughput; Structure-property relationship; Pathogen attachment; Sparse Bayesian methods; Medical devices; Nosocomial infectionsen_US
dc.titleModelling and prediction of bacterial attachment to polymersen_US
dc.status.refereedYesen_US
dc.date.Accepted2013
dc.date.application2013-12-04
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
dc.identifier.doihttps://doi.org/10.1002/adfm.201302877
refterms.dateFOA2018-07-28T03:08:52Z


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