Modelling and prediction of bacterial attachment to polymers

Vidana Chandana Epa, Andrew L Hook, Chien-yi Chang, Jing Yang, Robert Samuel M Langer, Daniel G Anderson, Paul Mickey Williams, Martyn C Davies, Morgan R Alexander, David Alan Winkler

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43 Citations (Scopus)


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. 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
Original languageEnglish
Pages (from-to)2085 - 2093
Number of pages9
JournalAdvanced Functional Materials
Issue number14
Publication statusPublished - 2014

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