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Home / Archives for Zazzi M

Determinants of HIV-1 Late Presentation in Patients Followed in Europe

  • Autores: Miranda MNS, Pingarilho M, Pimentel V, Martins MDRO, Vandamme A-M, Bobkova M, Böhm M, Seguin-Devaux C, Paredes R, Rubio R, Zazzi M, Incardona F, Abecasis A.
  • Ano de Publicação: 2021
  • Journal: Pathogens, 10(7), art 835
  • Link: https://doi.org/10.3390/pathogens10070835

ABSTRACT To control the Human Immunodeficiency Virus (HIV) pandemic, the World Health Organization (WHO) set the 90-90-90 target to be reached by 2020. One major threat to those goals is late presentation, which is defined as an individual presenting a TCD4+ count lower than 350 cells/mm3 or an AIDS-defining event. The present study aims to […]
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The effect of primary drug resistance on CD4 cell decline and the viral load set-point in HIV-positive individuals before the start of antiretroviral therapy

  • Autores: Schultze A, Torti C, Cozzi-Lepri A, Vandamme AM, Zazzi M, Sambatakou H, De Luca A, Geretti AM, Sönnerborg A, Ruiz L, Monno L, Di Giambenedetto S, Gori A, Lapadula G
  • Ano de Publicação: 2019
  • Journal: AIDS
  • Link: https://www.ncbi.nlm.nih.gov/pubmed/30325769

OBJECTIVE: To evaluate the effect of primary resistance and selected polymorphic amino-acid substitutions in HIV reverse transcriptase and protease on the CD4 cell count and viral load set point before the start of antiretroviral treatment. DESIGN: Prospective cohort study. METHODS: A total of 6180 individuals with a resistance test prior to starting antiretroviral treatment accessing […]
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Comparison of HIV-1 Genotypic Resistance Test Interpretation Systems in Predicting Virological Outcomes Over Time

  • Autores: Assel M, Boucher CA, De Luca A, Fabbiani M, Frentz D, Incardona F, Manca N, Müller V, O Nualláin B, Paredes R, Prosperi M, Quiros-Roldan E, Ruiz L, Sloot PM, Torti C, Van de Vijver DA, Zazzi M, Libin P, Vandamme AM, Van Laethem K
  • Journal: PLoS One
  • Link: http://www.ncbi.nlm.nih.gov/pubmed/?term=Comparison+of+HIV-1+Genotypic+Resistance+Test+Interpretation+Systems+in+Predicting+Virological+Outcomes+Over+Time

BACKGROUND: Several decision support systems have been developed to interpret HIV-1 drug resistance genotyping results. This study compares the ability of the most commonly used systems (ANRS, Rega, and Stanford’s HIVdb) to predict virological outcome at 12, 24, and 48 weeks. METHODOLOGY/PRINCIPAL FINDINGS: Included were 3763 treatment-change episodes (TCEs) for which a HIV-1 genotype was […]
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Declining prevalence of HIV-1 drug resistance in antiretroviral treatment-exposed individuals in Western Europe

  • Autores: Asboe D, Bansi L, Camacho R, Codoñer FM, De Luca A, Di Giambenedetto S, Dunn D, Fanti I, Ghisetti V, Kaiser R, Prosperi MCF, Sönnerborg A, Torti C, van de Vijver DC, Van Laethem K, Vandamme AM, Zazzi M
  • Ano de Publicação: 2013
  • Journal: Journal of Infectious Diseases
  • Link: http://jid.oxfordjournals.org/content/early/2013/01/11/infdis.jit017.abstract

HIV-1 drug resistance represents a major obstacle to infection and disease control. This retrospective study analyzes trends and determinants of resistance in antiretroviral treatment (ART)-exposed individuals across 7 countries in Europe. Of 20,323 cases, 80% carried at least one resistance mutation: these declined from 81% in 1997 to 71% in 2008. Predicted extensive 3-class resistance was rare (3.2% considering the cumulative genotype) and peaked at 4.5% in 2005, decreasing thereafter. The proportion of cases exhausting available drug options dropped from 32% in 2000 to 1% in 2008. Reduced risk of resistance over calendar years was confirmed by multivariable analysis.
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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

  • Autores: 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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About GHTM

GHTM is a R&D Center that brings together researchers from IHMT with a track record in Tropical Medicine and International/Global Health. It aims at strengthening Portugal's role as a leading partner in the development and implementation of a global health research agenda. Our evidence-based interventions contribute to the promotion of equity in health and to improve the health of populations.

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