ReviewViruses2012
Modelling the course of an HIV infection: insights from ecology and evolution.
Review in Viruses, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 70 citations in OpenAlex.
- Mcl-1 Protein and Viral Infections: A Narrative Review.International journal of molecular sciences · 2024Review
- Evolution during primary HIV infection does not require adaptive immune selection.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- RV144 vaccine imprinting constrained HIV-1 evolution following breakthrough infection.Virus evolution · 2021Article
- An Evolutionary Model of Progression to AIDS.Microorganisms · 2020Article
- Impact of Suboptimal APOBEC3G Neutralization on the Emergence of HIV Drug Resistance in Humanized Mice.Journal of virology · 2020Article
- A dynamic Bayesian Markov model for health economic evaluations of interventions in infectious disease.BMC medical research methodology · 2018Article
- Time Intervals in Sequence Sampling, Not Data Modifications, Have a Major Impact on Estimates of HIV Escape Rates.Viruses · 2018Article
- Mathematical Analysis of Viral Replication Dynamics and Antiviral Treatment Strategies: From Basic Models to Age-Based Multi-Scale Modeling.Frontiers in microbiology · 2018Review
- Modelling Optimal Control of In-Host HIV Dynamics Using Different Control Strategies.Computational and mathematical methods in medicine · 2018Article
- Effect of the Latent Reservoir on the Evolution of HIV at the Within- and Between-Host Levels.PLoS computational biology · 2017Article
- Human Immunodeficiency Virus Immune Cell Receptors, Coreceptors, and Cofactors: Implications for Prevention and Treatment.AIDS patient care and STDs · 2016Review
- Hybrid spreading mechanisms and T cell activation shape the dynamics of HIV-1 infection.PLoS computational biology · 2015Article
- Computational Studies of the Intestinal Host-Microbiota Interactome.Computation (Basel, Switzerland) · 2015Article
- Disentangling human tolerance and resistance against HIV.PLoS biology · 2014Article
- Virulence and pathogenesis of HIV-1 infection: an evolutionary perspective.Science (New York, N.Y.) · 2014Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Human Immunodeficiency Virus (HIV) is one of the most threatening viral agents. This virus infects approximately 33 million people, many of whom are unaware of their status because, except for flu-like symptoms right at the beginning of the infection during the acute phase, the disease progresses more or less symptom-free for 5 to 10 years. During this asymptomatic phase, the virus slowly destroys the immune system until the onset of AIDS when opportunistic infections like pneumonia or Kaposi's sarcoma can overcome immune defenses. Mathematical models have played a decisive role in estimating important parameters (e.g., virion clearance rate or life-span of infected cells). However, most models only account for the acute and asymptomatic latency phase and cannot explain the progression to AIDS. Models that account for the whole course of the infection rely on different hypotheses to explain the progression to AIDS. The aim of this study is to review these models, present their technical approaches and discuss the robustness of their biological hypotheses. Among the few models capturing all three phases of an HIV infection, we can distinguish between those that mainly rely on population dynamics and those that involve virus evolution. Overall, the modeling quest to capture the dynamics of an HIV infection has improved our understanding of the progression to AIDS but, more generally, it has also led to the insight that population dynamics and evolutionary processes can be necessary to explain the course of an infection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.