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Mathematical model predicts anti-adhesion-antibiotic-debridement combination therapies can clear an antibiotic resistant infection.

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Title: Mathematical model predicts anti-adhesion-antibiotic-debridement combination therapies can clear an antibiotic resistant infection.
Authors: Roberts PA; School of Mathematics, University of Birmingham, Edgbaston, Birmingham, United Kingdom.; Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom., Huebinger RM; Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America., Keen E; Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom., Krachler AM; Department of Microbiology and Molecular Genetics, University of Texas McGovern Medical School at Houston, Houston, Texas, United States of America., Jabbari S; School of Mathematics, University of Birmingham, Edgbaston, Birmingham, United Kingdom.; Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
Source: PLoS computational biology [PLoS Comput Biol] 2019 Jul 23; Vol. 15 (7), pp. e1007211. Date of Electronic Publication: 2019 Jul 23 (Print Publication: 2019).
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Mathematical model predicts anti-adhesion-antibiotic-debridement combination therapies can clear an antibiotic resistant infection.
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  Data: <searchLink fieldCode="AU" term="%22Roberts+PA%22">Roberts PA</searchLink>; School of Mathematics, University of Birmingham, Edgbaston, Birmingham, United Kingdom.; Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Huebinger+RM%22">Huebinger RM</searchLink>; Department of Surgery, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America.<br /><searchLink fieldCode="AU" term="%22Keen+E%22">Keen E</searchLink>; Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom.<br /><searchLink fieldCode="AU" term="%22Krachler+AM%22">Krachler AM</searchLink>; Department of Microbiology and Molecular Genetics, University of Texas McGovern Medical School at Houston, Houston, Texas, United States of America.<br /><searchLink fieldCode="AU" term="%22Jabbari+S%22">Jabbari S</searchLink>; School of Mathematics, University of Birmingham, Edgbaston, Birmingham, United Kingdom.; Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom.
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  Data: <searchLink fieldCode="JN" term="%22101238922%22">PLoS computational biology</searchLink> [PLoS Comput Biol] 2019 Jul 23; Vol. 15 (7), pp. e1007211. <i>Date of Electronic Publication: </i>2019 Jul 23 (<i>Print Publication: </i>2019).
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        Value: 10.1371/journal.pcbi.1007211
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              Text: 2019 Jul 23
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