| dc.contributor |
Hook, Andrew |
|
| dc.contributor |
Dundas, Adam |
|
| dc.contributor |
Irvine, Derek |
|
| dc.contributor |
Sanni, Olutoba |
|
| dc.contributor |
Anderson, Daniel |
|
| dc.contributor |
Langer, Robert |
|
| dc.contributor |
Williams, Paul |
|
| dc.creator |
Alexander, Morgan |
|
| dc.creator |
Mikulskis, Paulius |
|
| dc.creator |
Winkler, David |
|
| dc.date |
2018-12-21T17:04:37Z |
|
| dc.date |
2018-12-21T17:04:37Z |
|
| dc.date |
2018-12-21 |
|
| dc.identifier |
https://rdmc.nottingham.ac.uk/handle/internal/344 |
|
| dc.identifier |
http://doi.org/10.17639/nott.340 |
|
| dc.description |
Bacterial 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. |
|
| dc.language |
en |
|
| dc.publisher |
The University of Nottingham |
|
| dc.relation |
10.1021/acsami.7b14197 |
|
| dc.subject |
Medical instruments and apparatus -- Design and construction |
|
| dc.subject |
Medical instruments and apparatus -- Microbiology |
|
| dc.subject |
Machine learning |
|
| dc.subject |
Microbiology |
|
| dc.subject |
Materials -- Research |
|
| dc.subject |
Equipment Design |
|
| dc.subject |
Equipment and Supplies -- microbiology |
|
| dc.subject |
Artificial Intelligence |
|
| dc.subject |
Microbiology |
|
| dc.subject |
Biocompatible Materials |
|
| dc.subject |
fluorescence, polymers, pathogens, antimicrobial surfaces, QSAR, machine learning |
|
| dc.subject |
Physical sciences::Materials science |
|
| dc.subject |
Physical sciences::Chemistry::Organic chemistry::Polymer chemistry |
|
| dc.subject |
Physical sciences::Chemistry::Physical chemistry |
|
| dc.subject |
Q Science::QR Microbiology::QR 75 Bacteria. Cyanobacteria |
|
| dc.subject |
Q Science::QD Chemistry::QD241 Organic chemistry::QD415 Biochemistry |
|
| dc.subject |
Q Science::QD Chemistry::QD450 Physical and theoretical chemistry |
|
| dc.title |
Prediction of broad spectrum pathogen attachment to coating materials for biomedical devices data |
|
| dc.type |
dataset |
|