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Record W1992463335 · doi:10.4161/hv.6.4.10867

Development of TNFSF as molecular adjuvants for ALVAC HIV-1 vaccines

2010· article· en· W1992463335 on OpenAlexafffund
Jun Liu, Mario Ostrowski

Bibliographic record

VenueHuman Vaccines · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchOntario HIV Treatment Network
KeywordsImmunogenicityMedicineHIV vaccineAdjuvantVaccinationClinical trialImmunologyAIDS VaccinesVaccine trialVirologyHuman immunodeficiency virus (HIV)Immune systemInternal medicine

Abstract

fetched live from OpenAlex

A phase III clinical trial finished in Thailand recently showed that an ALVAC HIV-1 vaccine prime-gp120 protein boost vaccination regimen could modestly protect persons from HIV-1 infection, demonstrating that development of an effective and safe HIV-1 preventive vaccine is possible. ALVAC HIV-1 vaccines are candidate HIV-1 vaccines based on canarypox vectors. Previous clinical trials proved that ALVAC HIV-1 vaccines were safe but weak in immunogenicity when used in human subjects. We have been exploring to use tumor necrosis factor superfamily (TNFSF) members as adjuvants to enhance the immunogenicity of ALVAC HIV-1 vaccines. In this commentary, we will summarize our findings in using two TNFSF molecules, CD40L and OX40L, as adjuvants for an ALVAC HIV-1 vaccine in mouse model. We will also briefly discuss the challenges and prospects of using TNFSF molecules as adjuvants for HIV-1 vaccines in humans.

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)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.303
Teacher spread0.285 · 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 designBench or experimental
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

Citations15
Published2010
Admission routes2
Has abstractyes

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