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Record W2789594768 · doi:10.3389/fimmu.2018.00345

Virus-Like Particle, Liposome, and Polymeric Particle-Based Vaccines against HIV-1

2018· review· en· W2789594768 on OpenAlexafffund
Yong Gao, Chanuka N. Wijewardhana, Jamie F. S. Mann

Bibliographic record

VenueFrontiers in Immunology · 2018
Typereview
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsLiposomeVirologyParticle (ecology)Human immunodeficiency virus (HIV)Virus-like particleVirusMaterials scienceMedicineNanotechnologyChemistryBiologyRecombinant DNA

Abstract

fetched live from OpenAlex

It is acknowledged that vaccines remain the best hope for eliminating the HIV-1 epidemic. However, the failure to produce effective vaccine immunogens and the inability of conventional delivery strategies to elicit the desired immune responses remains a central theme and has ultimately led to a significant roadblock in HIV vaccine development. Consequently, significant efforts have been applied to generate novel vaccine antigens and delivery agents, which mimic viral structures for optimal immune induction. Here, we review the latest developments that have occurred in the nanoparticle vaccine field, with special emphasis on strategies that are being utilized to attain highly immunogenic, systemic, and mucosal anti-HIV humoral and cellular immune responses. This includes the design of novel immunogens, the central role of antigen-presenting cells, delivery routes, and biodistribution of nanoparticles to lymph nodes. In particular, we will focus on virus-like-particle formulations and their preclinical uses within the HIV prophylactic vaccine setting.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.019
GPT teacher head0.275
Teacher spread0.256 · 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 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

Citations77
Published2018
Admission routes2
Has abstractyes

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