Literature DB >> 19635505

Variations in MIC value caused by differences in experimental protocol.

J Merijn Schuurmans1, Anmar S Nuri Hayali, Belinda B Koenders, Benno H ter Kuile.   

Abstract

The minimal inhibitory concentration (MIC) of an antibiotic for a microorganism quantifies the effectiveness in reducing growth or the bactericidal ability of the compound. Measurements of MIC's carried out using different protocols should be comparable. Several of the factors that influence the outcome of the measurement vary between protocols. Variations in the MIC estimate were examined for E. coli and amoxicillin as well as tetracycline and for Pseudomonas putida and enrofloxacin. Duration of the measurement, density of the starting culture, the use of optical density or cell counts to determine growth and the induction of resistance can cause differences of a factor of up to 8 in the MIC value. While this does not hamper the reporting of trends by researchers adhering to the same protocol, it may affect assessments based on the absolute value of the MIC of a given combination of microorganism and antibiotic.

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Year:  2009        PMID: 19635505     DOI: 10.1016/j.mimet.2009.07.017

Source DB:  PubMed          Journal:  J Microbiol Methods        ISSN: 0167-7012            Impact factor:   2.363


  17 in total

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Journal:  Antimicrob Agents Chemother       Date:  2015-12-14       Impact factor: 5.191

2.  Development of a protocol for predicting bacterial resistance to microbicides.

Authors:  Laura Knapp; Alejandro Amézquita; Peter McClure; Sara Stewart; Jean-Yves Maillard
Journal:  Appl Environ Microbiol       Date:  2015-01-30       Impact factor: 4.792

3.  Interaction between mutations and regulation of gene expression during development of de novo antibiotic resistance.

Authors:  Nadine Händel; Jasper M Schuurmans; Yanfang Feng; Stanley Brul; Benno H ter Kuile
Journal:  Antimicrob Agents Chemother       Date:  2014-05-19       Impact factor: 5.191

4.  Modeling non-inherited antibiotic resistance.

Authors:  M C J Bootsma; M A van der Horst; T Guryeva; B H ter Kuile; O Diekmann
Journal:  Bull Math Biol       Date:  2012-05-19       Impact factor: 1.758

5.  Transfer dynamics of multi-resistance plasmids in Escherichia coli isolated from meat.

Authors:  Tania S Darphorn; Belinda B Koenders-van Sintanneland; Anita E Grootemaat; Nicole N van der Wel; Stanley Brul; Benno H Ter Kuile
Journal:  PLoS One       Date:  2022-07-07       Impact factor: 3.752

6.  Compensation of the metabolic costs of antibiotic resistance by physiological adaptation in Escherichia coli.

Authors:  Nadine Händel; J Merijn Schuurmans; Stanley Brul; Benno H ter Kuile
Journal:  Antimicrob Agents Chemother       Date:  2013-05-28       Impact factor: 5.191

7.  Dynamics of Mutations during Development of Resistance by Pseudomonas aeruginosa against Five Antibiotics.

Authors:  Yanfang Feng; Martijs J Jonker; Ioannis Moustakas; Stanley Brul; Benno H Ter Kuile
Journal:  Antimicrob Agents Chemother       Date:  2016-06-20       Impact factor: 5.191

8.  Influence of Reactive Oxygen Species on De Novo Acquisition of Resistance to Bactericidal Antibiotics.

Authors:  Marloes Hoeksema; Stanley Brul; Benno H Ter Kuile
Journal:  Antimicrob Agents Chemother       Date:  2018-05-25       Impact factor: 5.191

9.  Factors that affect transfer of the IncI1 β-lactam resistance plasmid pESBL-283 between E. coli strains.

Authors:  Nadine Händel; Sarah Otte; Martijs Jonker; Stanley Brul; Benno H ter Kuile
Journal:  PLoS One       Date:  2015-04-01       Impact factor: 3.240

10.  Experimental Simulation of the Effects of an Initial Antibiotic Treatment on a Subsequent Treatment after Initial Therapy Failure.

Authors:  Yanfang Feng; Nadine Händel; Marnix H P de Groot; Stanley Brul; Constance Schultsz; Benno H Ter Kuile
Journal:  Antibiotics (Basel)       Date:  2014-02-17
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