Predictors of non adherence to antiretroviral therapy at an urban HIV care and treatment center in Tanzania

Authors: Aboud S, Chalamilla G, Kamuhabwa A, Lyamuya EF, Mosha F, Sangeda RZ, Van Wijngaerden E, Vandamme AM, Vercauteren J
Publication Year: 2018
Journal: Drug Healthcare and Patient Safety
Link: https://www.ncbi.nlm.nih.gov/pubmed/30174460

BACKGROUND: Measurement of adherence to antiretroviral therapy (ART) can serve as a proxy for virologic failure in resource-limited settings. The aim of this study was to determine the factors underlying nonadherence measured by three methods. PATIENTS AND METHODS: This is a prospective longitudinal cohort of 220 patients on ART at Amana Hospital in Dar es […]
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Gender differences in HIV disease progression and treatment outcomes among HIV patients one year after starting antiretroviral treatment (ART) in Dar es Salaam, Tanzania

Authors: Lyamuya E, Matee M, Mosha F, Muchunguzi V, Nsubuga P, Sangeda RZ, Vandamme AM, Vercauteren J
Publication Year: 2013
Journal: BMC Public Health
Link: http://www.biomedcentral.com/1471-2458/13/38

We investigated gender differences in treatment outcome during first line antiretroviral treatment (ART) in a hospital setting in Tanzania, assessing clinical, social demographic, virological and immunological factors.
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HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

Authors: Beheydt G, Bruzzone B, Camacho RJ, De Luca A, Deforche K, Grossman Z, Imbrechts S, Incardona F, Libin P, Pironti A, Rhee SY, Ruiz L, Sangeda RZ, Shafer RW, Sönnerborg A, Theys K, Torti C, Van de Vijver DA, Van Laethem K, Van Wijngaerden E, Vandamme AM, Vercauteren J, Zazzi M
Journal: Infection Genetics and Evolution
Link: http://www.ncbi.nlm.nih.gov/pubmed/23523594

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment.
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Predictors of non adherence to antiretroviral therapy at an urban HIV care and treatment center in Tanzania

Gender differences in HIV disease progression and treatment outcomes among HIV patients one year after starting antiretroviral treatment (ART) in Dar es Salaam, Tanzania

HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure

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