TY - JOUR
T1 - Modelling and prediction of bacterial attachment to polymers
AU - Epa, Vidana Chandana
AU - Hook, Andrew L
AU - Chang, Chien-yi
AU - Yang, Jing
AU - Langer, Robert Samuel M
AU - Anderson, Daniel G
AU - Williams, Paul Mickey
AU - Davies, Martyn C
AU - Alexander, Morgan R
AU - Winkler, David Alan
PY - 2014
Y1 - 2014
N2 - 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
AB - 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
UR - http://onlinelibrary.wiley.com/doi/10.1002/adfm.201302877/epdf
U2 - 10.1002/adfm.201302877
DO - 10.1002/adfm.201302877
M3 - Article
SN - 1616-301X
VL - 24
SP - 2085
EP - 2093
JO - Advanced Functional Materials
JF - Advanced Functional Materials
IS - 14
ER -