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The basic process
Evolution of Antibiotic Resistance
Elements for Prediction
Emergence of mutational resistance
Complexity in prediction of mutation rate
Target structural mutations (1)
Target structural mutations (2)
Prediction of antibiotic-resistance theoretical mutation rate
Process of sequential selection of intermediate and resistant variants
Antibiotic Gradients in Compartmentalized Habitats
Concentration-Dependent Selection of TEM-12 over TEM-1 (mixed cultures1:100)
Time-dependent Selection of TEM-12 and TEM-12/OmpF over TEM-1 in mixed cultures
TEM-12 selection over TEM-1 in mice treated with cefotaxime: change in log TEM-12/TEM1
P. aeruginosa mutation rates in cystic fibrosis and bacteremic patients
Antibiotic Resistance in mutator phenotype P. aeruginosa from cystic fibrosis patients
PPT Slide
Why mutators do not predominate?
Biological Cost of Low-level Resistance may be Compensated before Evolution to High-level Resistancel
Conditions that increases the rate of antibiotic-R mutants (I)
Conditions that increases the rate of antibiotic-R mutants (II)
Hungry predictive mathematical models
Hungry models for resistance: what do we need? Most models are based on: 1. Duration of infectiousness of infected individuals 2. Incidence of drug treatment 3. Extent to which treatment of susceptible population reduces the transmission of the infection 4. Degree of reduction in fitness of the resistant bacteria in the absence of treatment (cost) 5. Probability of acquisition of resistance during therapy. (Science, 283:808, 1999)
The 15 essential components in the predictive modeling of development of antibiotic resistance (1)
The 15 essential components of the predictive modeling of development of antibiotic resistance (2)
Some parameters used in the study of Iceland S. pneumoniae pen-R
The patient and the community: the unified view
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