Mixed-Strain Mycobacterium tuberculosis Infections and the Implications for Tuberculosis Treatment and Control.
ABSTRACT Numerous studies have reported that individuals can simultaneously harbor multiple distinct strains of Mycobacterium tuberculosis. To date, there has been limited discussion of the consequences for the individual or the epidemiological importance of mixed infections. Here, we review studies that documented mixed infections, highlight challenges associated with the detection of mixed infections, and discuss possible implications of mixed infections for the diagnosis and treatment of patients and for the community impact of tuberculosis control strategies. We conclude by highlighting questions that should be resolved in order to improve our understanding of the importance of mixed-strain M. tuberculosis infections.
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ABSTRACT: High resolution tests for genetic variation reveal that individuals may simultaneously host more than one distinct strain of M. tuberculosis. Previous studies find that this phenomenon, which we will refer to as "mixed infection", may affect the outcomes of treatment for infected individuals and may influence the impact of population-level interventions against tuberculosis. In areas where the incidence of TB is high, mixed infections have been found in nearly 20% of patients; these studies may underestimate the actual prevalence of mixed infection given that tests may not be sufficiently sensitive for detecting minority strains. Specific reasons for failing to detect mixed infections would include low initial numbers of minority strain cells in sputum, stochastic growth in culture and the physical division of initial samples into parts (typically only one of which is genotyped). In this paper, we develop a mathematical framework that models the study designs aimed to detect mixed infections. Using both a deterministic and a stochastic approach, we obtain posterior estimates of the prevalence of mixed infection. We find that the posterior estimate of the prevalence of mixed infection may be substantially higher than the fraction of cases in which it is detected. We characterize this bias in terms of the sensitivity of the genotyping method and the relative growth rates and initial population sizes of the different strains collected in sputum. Copyright © 2014. Published by Elsevier Ltd.Journal of Theoretical Biology 12/2014; 368. DOI:10.1016/j.jtbi.2014.12.009 · 2.35 Impact Factor
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ABSTRACT: Background. New first-line drug regimens for treatment of tuberculosis (TB) are in clinical trials: emergence of resistance is a key concern. Because population-level data on resistance cannot be collected in advance, epidemiological models are important tools for understanding the drivers and dynamics of resistance before novel drug regimens are launched. Methods. We developed a transmission model of TB after launch of a new drug regimen, defining drug-resistant TB (DR-TB) as resistance to the new regimen. The model is characterized by (1) the probability of acquiring resistance during treatment, (2) the transmission fitness of DR-TB relative to drug-susceptible TB (DS-TB), and (3) the probability of treatment success for DR-TB versus DS-TB. We evaluate the effect of each factor on future DR-TB prevalence, defined as the proportion of incident TB that is drug-resistant. Results. Probability of acquired resistance was the strongest predictor of the DR-TB proportion in the first 5 years after the launch of a new drug regimen. Over a longer term, however, the DR-TB proportion was driven by the resistant population's transmission fitness and treatment success rates. Regardless of uncertainty in acquisition probability and transmission fitness, high levels (>10%) of drug resistance were unlikely to emerge within 50 years if, among all cases of TB that were detected, 85% of those with DR-TB could be appropriately diagnosed as such and then successfully treated. Conclusions. Short-term surveillance cannot predict long-term drug resistance trends after launch of novel first-line TB regimens. Ensuring high treatment success of drug-resistant TB through early diagnosis and appropriate second-line therapy can mitigate many epidemiological uncertainties and may substantially slow the emergence of drug-resistant TB.07/2014; 1(2):ofu073. DOI:10.1093/ofid/ofu073
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ABSTRACT: We measure dc voltages in a permalloy (Py) single layer and a Py/Pt bilayer under ferromagnetic resonance. The observed voltages can be decomposed into symmetric and antisymmetric components. First, we confirm for Py that the symmetric component results from the planar Hall effect (PHE), whereas the antisymmetric one results from the anomalous Hall effect. Then, we consider the dc voltage induced by the inverse spin Hall effect (ISHE) in Py/Pt superimposing the galvanomagnetic effects, and conclude that the voltage induced by ISHE also contributes to the symmetric component with about 2.5 times smaller magnitude than that by PHE. Our results indicate that one should be careful about the galvanomagnetic effects in the quantitative analyses of dc voltage induced by spin pumping. (C) 2014 The Japan Society of Applied PhysicsApplied Physics Express 01/2014; 7(1):013002. DOI:10.7567/APEX.7.013002 · 2.57 Impact Factor