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The Role of Antibodies in HIV Vaccines

2010· review· en· W2171536421 on OpenAlexfundno aff
John R. Mascola, David C. Montefiori

Bibliographic record

VenueAnnual Review of Immunology · 2010
Typereview
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsnot available
FundersYork University
KeywordsVirologyAntibodyBiologyEpitopeImmunologyVirusNeutralizing antibodyViral envelopeAIDS VaccinesAntibody-dependent enhancementHIV vaccineVaccine trialViral replication

Abstract

fetched live from OpenAlex

Licensed vaccines against viral diseases generate antibodies that neutralize the infecting virus and protect against infection or disease. Similarly, an effective vaccine against HIV-1 will likely need to induce antibodies that prevent initial infection of host cells or that limit early events of viral dissemination. Such antibodies must target the surface envelope glycoproteins of HIV-1, which are highly variable in sequence and structure. The first subunit vaccines to enter clinical trails were safe and immunogenic but unable to elicit antibodies that neutralized most circulating strains of HIV-1. However, potent virus neutralizing antibodies (NAbs) can develop during the course of HIV-1 infection, and this is the type of antibody response that researchers seek to generate with a vaccine. Thus, current vaccine design efforts have focused on a more detailed understanding of these broadly neutralizing antibodies and their epitopes to inform the design of improved vaccines.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.005

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 head0.012
GPT teacher head0.327
Teacher spread0.314 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations385
Published2010
Admission routes1
Has abstractyes

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