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Record W2752141707 · doi:10.1186/s12981-017-0178-3

Development of an anti-HIV vaccine eliciting broadly neutralizing antibodies

2017· review· en· W2752141707 on OpenAlexafffund
Yousuf Ahmed, Meijuan Tian, Yong Gao

Bibliographic record

VenueAIDS Research and Therapy · 2017
Typereview
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsWestern University
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsHIV vaccineHuman immunodeficiency virus (HIV)VirologyAntibodyAIDS VaccinesMedicineVaccinationImmunologyImmune systemNeutralizing antibodyAntibody responseTransmission (telecommunications)Vaccine trialComputer science

Abstract

fetched live from OpenAlex

The extreme HIV diversity posts a great challenge on development of an effective anti-HIV vaccine. To solve this problem, it is crucial to discover an appropriate immunogens and strategies that are able to prevent the transmission of the diverse viruses that are circulating in the world. Even though there have been a number of broadly neutralizing anti-HIV antibodies (bNAbs) been discovered in recent years, induction of such antibodies to date has only been observed in HIV-1 infection. Here, in this mini review, we review the progress in development of HIV vaccine in eliciting broad immune response, especially production of bNAbs, discuss possible strategies, such as polyvalent sequential vaccination, that facilitates B cell maturation leading to bNAb response.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.240
GPT teacher head0.472
Teacher spread0.232 · 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 teacher head, not a consensus.

Study designOther design
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

Citations34
Published2017
Admission routes2
Has abstractyes

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