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Modelling and Prediction of Bacterial Attachment to Polymers

Lookup NU author(s): Dr Chien-Yi ChangORCiD

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by Wiley - VCH Verlag GmbH & Co. KGaA, 2014.

For re-use rights please refer to the publisher's terms and conditions.

Abstract

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.

Publication metadata

Author(s): Epa VC, Hook AL, Chang C, Yang J, Langer R, Anderson DG, Williams P, Davies MC, Alexander MR, Winkler DA

Publication type: Article

Publication status: Published

Journal: Advanced Functional Materials

Year: 2014

Volume: 24

Issue: 14

Pages: 2085-2093

Print publication date: 09/04/2014

Online publication date: 09/04/2014

Acceptance date: 04/12/2014

Date deposited: 06/08/2020

ISSN (print): 1616-301X

ISSN (electronic): 1616-3028

Publisher: Wiley - VCH Verlag GmbH & Co. KGaA

URL: http://dx.doi.org/10.1002/adfm.201302877

DOI: 10.1002/adfm.201302877

Notes: epub:04/12/2013

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