Literature DB >> 3433231

Antibiotic exposure and resistance in mixed bacterial populations.

A M Garber1.   

Abstract

Antibiotic use is often blamed for increases in the prevalence of infections due to antibiotic-resistance bacteria. This paper clarifies the effects of antibiotic exposure on bacterial antibiotic resistance by developing models that describe the growth of competing bacterial strains whose antibiotic sensitivities differ. The analysis generalizes logistic growth models to include first-order growth parameters that are arbitrary functions of antibiotic levels. It derives closed-form solutions for population size, composition, and average antibiotic sensitivities as functions of antibiotic exposure. Strategies to minimize the bacterial population size are analyzed in the context of the model. These heuristic models explore in formal terms the population dynamics thought to underlie resistance development.

Entities:  

Mesh:

Substances:

Year:  1987        PMID: 3433231     DOI: 10.1016/0040-5809(87)90053-0

Source DB:  PubMed          Journal:  Theor Popul Biol        ISSN: 0040-5809            Impact factor:   1.570


  4 in total

1.  Minimum antibiotic levels for selecting a resistance plasmid in a gnotobiotic animal model.

Authors:  D E Corpet; S Lumeau; F Corpet
Journal:  Antimicrob Agents Chemother       Date:  1989-04       Impact factor: 5.191

2.  How to Use a Chemotherapeutic Agent When Resistance to It Threatens the Patient.

Authors:  Elsa Hansen; Robert J Woods; Andrew F Read
Journal:  PLoS Biol       Date:  2017-02-09       Impact factor: 8.029

3.  How fitness reduced, antimicrobial resistant bacteria survive and spread: a multiple pig-multiple bacterial strain model.

Authors:  Kaare Græsbøll; Søren Saxmose Nielsen; Nils Toft; Lasse Engbo Christiansen
Journal:  PLoS One       Date:  2014-07-09       Impact factor: 3.240

4.  Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas.

Authors:  Elsje Pienaar; Jennifer J Linderman; Denise E Kirschner
Journal:  PLoS One       Date:  2018-05-10       Impact factor: 3.240

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.