The Role of Exposure History on HIV Acquisition: Insights from Repeated Low-dose Challenge Studies
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{"title"=>"The Role of Exposure History on HIV Acquisition: Insights from Repeated Low-dose Challenge Studies", "type"=>"journal", "authors"=>[{"first_name"=>"Roland R.", "last_name"=>"Regoes", "scopus_author_id"=>"6603264178"}], "year"=>2012, "source"=>"PLoS Computational Biology", "identifiers"=>{"isbn"=>"3152301308", "pmid"=>"23180981", "pui"=>"366216199", "issn"=>"1553734X", "scopus"=>"2-s2.0-84870717488", "sgr"=>"84870717488", "doi"=>"10.1371/journal.pcbi.1002767"}, "id"=>"1c941752-7c6d-375d-8ddb-ed6b38ec3893", "abstract"=>"To assess the efficacy of HIV vaccine candidates or preventive treatment, many research groups have started to challenge monkeys repeatedly with low doses of the virus. Such challenge data provide a unique opportunity to assess the importance of exposure history for the acquisition of the infection. I developed stochastic models to analyze previously published challenge data. In the mathematical models, I allowed for variation of the animals' susceptibility to infection across challenge repeats, or across animals. In none of the studies I analyzed, I found evidence for an immunizing effect of non-infecting challenges, and in most studies, there is no evidence for variation in the susceptibilities to the challenges across animals. A notable exception was a challenge experiment by Letvin et al. Sci Translat Med (2011) conducted with the strain SIVsmE660. The challenge data of this experiment showed significant susceptibility variation from animal-to-animal, which is consistent with previously established genetic differences between the involved animals. For the studies which did not show significant immunizing effects and susceptibility differences, I conducted a power analysis and could thus exclude a very strong immunization effect for some of the studies. These findings validate the assumption that non-infecting challenges do not immunize an animal - an assumption that is central in the argument that repeated low-dose challenge experiments increase the statistical power of preclinical HIV vaccine trials. They are also relevant for our understanding of the role of exposure history for HIV acquisition and forecasting the epidemiological spread of HIV.", "link"=>"http://www.mendeley.com/research/role-exposure-history-hiv-acquisition-insights-repeated-lowdose-challenge-studies", "reader_count"=>16, "reader_count_by_academic_status"=>{"Researcher"=>4, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Student > Master"=>1, "Student > Bachelor"=>2, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Unspecified"=>1}, "reader_count_by_user_role"=>{"Researcher"=>4, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Student > Master"=>1, "Student > Bachelor"=>2, "Lecturer"=>2, "Lecturer > Senior Lecturer"=>1, "Unspecified"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>1, "Medicine and Dentistry"=>5, "Agricultural and Biological Sciences"=>6, "Unspecified"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Portugal"=>1, "India"=>1}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/291696", "https://ndownloader.figshare.com/files/291706"], "description"=>"<div><p>To assess the efficacy of HIV vaccine candidates or preventive treatment, many research groups have started to challenge monkeys repeatedly with low doses of the virus. Such challenge data provide a unique opportunity to assess the importance of exposure history for the acquisition of the infection. I developed stochastic models to analyze previously published challenge data. In the mathematical models, I allowed for variation of the animals' susceptibility to infection across challenge repeats, or across animals. In none of the studies I analyzed, I found evidence for an immunizing effect of non-infecting challenges, and in most studies, there is no evidence for variation in the susceptibilities to the challenges across animals. A notable exception was a challenge experiment by Letvin et al. Sci Translat Med (2011) conducted with the strain SIVsmE660. The challenge data of this experiment showed significant susceptibility variation from animal-to-animal, which is consistent with previously established genetic differences between the involved animals. For the studies which did not show significant immunizing effects and susceptibility differences, I conducted a power analysis and could thus exclude a very strong immunization effect for some of the studies. These findings validate the assumption that non-infecting challenges do not immunize an animal — an assumption that is central in the argument that repeated low-dose challenge experiments increase the statistical power of preclinical HIV vaccine trials. They are also relevant for our understanding of the role of exposure history for HIV acquisition and forecasting the epidemiological spread of HIV.</p> </div>", "links"=>[], "tags"=>["hiv", "insights", "repeated", "low-dose", "studies"], "article_id"=>117430, "categories"=>["Biological Sciences", "Cancer", "Immunology"], "users"=>["Roland R. Regoes"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002767.s001", "https://dx.doi.org/10.1371/journal.pcbi.1002767.s002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_Role_of_Exposure_History_on_HIV_Acquisition_Insights_from_Repeated_Low_dose_Challenge_Studies__/117430", "title"=>"The Role of Exposure History on HIV Acquisition: Insights from Repeated Low-dose Challenge Studies", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-11-08 02:03:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/544948"], "description"=>"<p>The step functions for each study terminate at the maximum number of challenges applied in this study, or when all animals are infected.</p>", "links"=>[], "tags"=>["animals", "studies", "considered"], "article_id"=>215445, "categories"=>["Biological Sciences", "Infectious Diseases", "Immunology"], "users"=>["Roland R. Regoes"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002767.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Challenge_data_of_the_control_animals_from_the_studies_5_6_8_10_13_considered_in_this_paper_/215445", "title"=>"Challenge data of the control animals from the studies [5], [6], [8]–[10], [13], considered in this paper.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:30:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/544990"], "description"=>"<p><b>A</b> the geometric infection model, <b>B</b> the immune priming model, and <b>C</b> the heterogeneous susceptibility model. The circles represent animals, and the darkness corresponds to their susceptibility. Crossed circles signify that an animal has become infected. The geometric infection model assumes equal susceptibilities across animals and challenge repeats. In the immune priming model, the susceptibilities decrease with each challenge received, but animals that received the same number of challenges have the same susceptibility (illustrated by the same level of grey along the animal axis). In the heterogeneous susceptibility model, the susceptibility is assumed to vary across animals, but not with challenge repeats (illustrated by the same level of grey along the challenge axis).</p>", "links"=>[], "tags"=>["immunology", "Infectious diseases", "Computational biology"], "article_id"=>215489, "categories"=>["Biological Sciences", "Infectious Diseases", "Immunology"], "users"=>["Roland R. Regoes"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002767.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Diagrammatic_representation_of_the_infection_models_/215489", "title"=>"Diagrammatic representation of the infection models.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:31:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/545027"], "description"=>"<p>The red dashed lines indicates the maximum of the likelihood.</p>", "links"=>[], "tags"=>["geometric"], "article_id"=>215530, "categories"=>["Biological Sciences", "Infectious Diseases", "Immunology"], "users"=>["Roland R. Regoes"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002767.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Likelihood_of_the_geometric_infection_model_for_the_different_datasets_/215530", "title"=>"Likelihood of the geometric infection model for the different datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:32:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/545069"], "description"=>"<p> The red dots indicate the maximum of the likelihood.</p>", "links"=>[], "tags"=>["plots", "priming"], "article_id"=>215571, "categories"=>["Biological Sciences", "Infectious Diseases", "Immunology"], "users"=>["Roland R. Regoes"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002767.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Contour_plots_of_likelihood_of_the_immune_priming_model_for_the_different_datasets_/215571", "title"=>"Contour plots of likelihood of the immune priming model () for the different datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:32:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/545122"], "description"=>"<p>The red dots indicate the maximum of the likelihood.</p>", "links"=>[], "tags"=>["plots", "heterogeneous", "susceptibility"], "article_id"=>215630, "categories"=>["Biological Sciences", "Infectious Diseases", "Immunology"], "users"=>["Roland R. Regoes"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002767.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Contour_plots_of_likelihood_of_the_heterogeneous_susceptibility_model_for_the_different_datasets_/215630", "title"=>"Contour plots of likelihood of the heterogeneous susceptibility model for the different datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:33:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/545197"], "description"=>"<p>Power estimates are shown as a function of the effect size. The <i>in silico</i> challenge data are generated according to an immune priming model in which the infection probability drops from an initial value of to a lower value after the first challenge (). Effect size is measured as the ratio between and . The different curves are generated with the same number of animals and maximum challenge repeats as the datasets indicated in the legend.</p>", "links"=>[], "tags"=>["experiments", "immunization"], "article_id"=>215696, "categories"=>["Biological Sciences", "Infectious Diseases", "Immunology"], "users"=>["Roland R. Regoes"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002767.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Power_of_the_experiments_to_establish_an_immunization_effect_/215696", "title"=>"Power of the experiments to establish an immunization effect.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:34:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/545245"], "description"=>"<p><b>A</b> Power estimates are shown as a function of the effect size measured by the variance parameter . The different curves are generated with the same number of animals and maximum challenge repeats as the datasets indicated in the legend. The dashed line shows the level of susceptibility variance (), for which an experiment with the same number of animals and maximum challenge repeats as the one in Hansen et al (2011) has a power larger than 0.95. <b>B</b> Histogram of a susceptibility distribution with . A susceptibility distribution more heterogeneous than the one shown can be ruled out with 95% probability.</p>", "links"=>[], "tags"=>["experiments", "susceptibility", "differences"], "article_id"=>215744, "categories"=>["Biological Sciences", "Infectious Diseases", "Immunology"], "users"=>["Roland R. Regoes"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002767.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Power_of_the_experiments_to_establish_susceptibility_differences_between_animals_/215744", "title"=>"Power of the experiments to establish susceptibility differences between animals.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-08 01:35:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/545291"], "description"=>"<p>CI abbreviates confidence interval. The last column gives the -values for a likelihood ratio test, and significant tests are marked by <sup>*</sup>.</p>", "links"=>[], "tags"=>["heterogeneous", "susceptibility", "geometric"], "article_id"=>215785, "categories"=>["Biological Sciences", "Infectious Diseases", "Immunology"], "users"=>["Roland R. Regoes"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002767.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_comparison_of_the_heterogeneous_susceptibility_model_to_the_geometric_infection_model_/215785", "title"=>"Statistical comparison of the heterogeneous susceptibility model to the geometric infection model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-08 01:36:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/545311"], "description"=>"<p>CI abbreviates confidence interval. The last column gives the -values for a likelihood ratio test, and significant tests are marked by <sup>*</sup>.</p>", "links"=>[], "tags"=>["priming", "models", "geometric"], "article_id"=>215816, "categories"=>["Biological Sciences", "Infectious Diseases", "Immunology"], "users"=>["Roland R. Regoes"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002767.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Statistical_comparison_of_the_immune_priming_models_to_the_geometric_infection_model_/215816", "title"=>"Statistical comparison of the immune priming models to the geometric infection model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-08 01:36:56"}
  • {"files"=>["https://ndownloader.figshare.com/files/545335"], "description"=>"<p>Fits of the geometric infection model.</p>", "links"=>[], "tags"=>["geometric"], "article_id"=>215837, "categories"=>["Biological Sciences", "Infectious Diseases", "Immunology"], "users"=>["Roland R. Regoes"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1002767.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fits_of_the_geometric_infection_model_/215837", "title"=>"Fits of the geometric infection model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-08 01:37:17"}
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PMC Usage Stats | Further Information

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