alexa Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis.
Medicine

Medicine

Internal Medicine: Open Access

Author(s): Shah HN, Rajakaruna L, Ball G, Misra R, AlShahib A,

Abstract Share this page

Abstract Strains (n=99) of Staphylococcus aureus isolated from a large number of clinical sources and tested for methicillin sensitivity were analysed by MALDI-TOF-MS using the Weak Cation Exchange (CM10) ProteinChip Array (designated SELDI-TOF-MS). The profile data generated was analysed using Artificial Neural Network (ANN) Analysis modelling techniques. Seven key ions identified by the ANNs that were predictive of MRSA and MSSA were validated by incorporation into a model. This model exhibited an area under the ROC curve value of 0.9147 indicating the potential application of this approach for rapidly characterising MRSA and MSSA isolates. Nearly all strains (n=97) were correctly assigned to the correct group, with only two aberrant MSSA strains being misclassified. However, approximately 21\% of the strains appeared to be in a process of transition as resistance to methicillin was being acquired. Copyright © 2010 Elsevier GmbH. All rights reserved. This article was published in Syst Appl Microbiol and referenced in Internal Medicine: Open Access

Relevant Expert PPTs

Relevant Speaker PPTs

Recommended Conferences

Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords