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dc.contributor.authorAlexander, Morgan
dc.contributor.authorMikulskis, Paulius
dc.contributor.authorWinkler, David
dc.contributor.otherHook, Andrew
dc.contributor.otherDundas, Adam
dc.contributor.otherIrvine, Derek
dc.contributor.otherSanni, Olutoba
dc.contributor.otherAnderson, Daniel
dc.contributor.otherLanger, Robert
dc.contributor.otherWilliams, Paul
dc.date.accessioned2018-12-21T17:04:37Z
dc.date.available2018-12-21T17:04:37Z
dc.date.issued2018-12-21
dc.identifier.urihttps://rdmc.nottingham.ac.uk/handle/internal/344
dc.identifier.urihttp://dx.doi.org/10.17639/nott.340
dc.description.abstractBacterial 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.en_UK
dc.language.isoenen_UK
dc.publisherThe University of Nottinghamen_UK
dc.subject.lcshMedical instruments and apparatus -- Design and constructionen_UK
dc.subject.lcshMedical instruments and apparatus -- Microbiologyen_UK
dc.subject.lcshMachine learningen_UK
dc.subject.lcshMicrobiologyen_UK
dc.subject.lcshMaterials -- Researchen_UK
dc.subject.meshEquipment Designen_UK
dc.subject.meshEquipment and Supplies -- microbiologyen_UK
dc.subject.meshArtificial Intelligenceen_UK
dc.subject.meshMicrobiologyen_UK
dc.subject.meshBiocompatible Materialsen_UK
dc.titlePrediction of broad spectrum pathogen attachment to coating materials for biomedical devices dataen_UK
dc.identifier.doi10.1021/acsami.7b14197en_UK
dc.subject.freefluorescence, polymers, pathogens, antimicrobial surfaces, QSAR, machine learningen_UK
dc.subject.jacsPhysical sciences::Materials scienceen_UK
dc.subject.jacsPhysical sciences::Chemistry::Organic chemistry::Polymer chemistryen_UK
dc.subject.jacsPhysical sciences::Chemistry::Physical chemistryen_UK
dc.subject.lcQ Science::QR Microbiology::QR 75 Bacteria. Cyanobacteriaen_UK
dc.subject.lcQ Science::QD Chemistry::QD241 Organic chemistry::QD415 Biochemistryen_UK
dc.subject.lcQ Science::QD Chemistry::QD450 Physical and theoretical chemistryen_UK
uon.divisionUniversity of Nottingham, UK Campus::Faculty of Science::School of Pharmacyen_UK
uon.funder.controlledEngineering & Physical Sciences Research Councilen_UK
uon.funder.controlledWellcome Trusten_UK
uon.datatypeDatasetsen_UK
uon.grantEP/N006615/1en_UK
uon.grant085245en_UK
uon.collectionmethodGenePix Autoloader 4200ALen_UK


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