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Record W2280991803 · doi:10.82308/51533

HIV-specific immunity Acute Infection Early disease (AIED)

2010· article· en· W2280991803 on OpenAlexaff
Ndongala Michel Lubaki

Bibliographic record

VenueOpen MIND · 2010
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsMcGill University
FundersNational Institutes of HealthStyrelsen för Internationellt Utvecklingssamarbete
KeywordsImmunityImmunologyMedicineDiseaseVirologyHuman immunodeficiency virus (HIV)Immune systemInternal medicine

Abstract

fetched live from OpenAlex

An estimated 33 millions of individuals are currently infected with HIV worldwide; the majority of them lives in Sub-Saharan Africa and do not have access to antiretroviral treatment despite all efforts from the international community. Despite efforts made in the area of prevention and treatment, the virus is still continuing to infect people worldwide. It is therefore believed that a safe, effective and affordable vaccine is the best solution to the global HIV epidemic. Unfortunately, despite recent advances in our understanding of HIV-1 pathogenesis and immunology, this goal remains elusive. Vaccination started with Edward Jenner's success with smallpox immunization in 1796. Since then, a number of successful vaccines have been developed including, polio, measles, mumps, rubella, hepatitis B and influenza. The quest for an effective HIV-1 vaccine has proved to be very difficult because of specific characteristic of the virus, the lack of understanding of correlates of protection from infection and disease progression and the inadequacy of assays currently used to evaluate immune responses. This thesis focuses on characterizing the qualitative and quantitative features of HIV-specific T cell immune responses in individuals in primary infection as well as their fate in progressive HIV disease. The rationale for studying individuals in primary HIV infection is that events during this phase of infection are believed to set the stage for the subsequent course of infection. We first developed an ELISPOT assay able to detect both IFN-γ and IL-2 secretion simultaneously. This assay was used for the comprehensive screening and characterization of responses to the entire HIV proteome in individuals during primary and chronic infections. We showed that the dual color ELISPOT assay developed in our laboratory is capable of detecting 3 functional lymphocyte populations: single IFN-γ, dual IFN-γ/IL-2 and single IL-2 secreting cells. We demonstrated that this assay is sensitive, reproducible and able to detect both CD4+ and CD8+ T cell responses. We found that the breadth and magnitude of responses directed against the entire HIV proteome was not associated with control of viremia. Interestingly, we found an association between Gag p55-, and particularly Gag p24-specific responses, with concurrent and set point VL, for all 3 functional subsets detected. We also showed that the contribution of IFN-γ/IL-2 secreting cells to the total HIV-specific response as well as their proliferative capacity was reduced by the 2nd year of HIV infection. Taken together, we believe that the results presented in this thesis contribute to furthering our understanding of immune correlates of protection from disease progression. These results suggest that vaccine strategies designed to focus immune responses against Gag may have a better chance of controlling HIV viral replication to levels that slow disease progression and reduce HIV transmission both at the individual and the population levels.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.010

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.374
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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