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Record W4394523494 · doi:10.6084/m9.figshare.24064138

Rhesus macaques show increased resistance to repeated SHIV intrarectal exposure following a heterologous regimen of rVSV vector vaccine expressing HIV antigen

2023· dataset· en· W4394523494 on OpenAlexaff
Joseph Jelinski, Monika M. Kowatsch, Marc-Alexandre Lafrance, Alice Berger, Jannie Pedersen, Hiva Azizi, Yue Li, Florine E. M. Scholte, Alejandro Gómez, Natasha A. Hollett, Toby Le, Matthew Wade, Hugues Fausther‐Bovendo, Marc-Antoine de La Vega, George Babuadze, Ara XIII, Claude Lamarre, Trina Racine, C. Yong Kang, Galit Alter, Eric J. Arts, Keith R. Fowke, Gary Kobinger

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsWestern UniversityUniversity of Manitoba
Fundersnot available
KeywordsVirologyHeterologousRegimenHuman immunodeficiency virus (HIV)Vector (molecular biology)AntigenMedicineImmunologyBiologyInternal medicineRecombinant DNAGeneticsGene

Abstract

fetched live from OpenAlex

Despite the human immunodeficiency virus (HIV) pandemic continuing worldwide for 40 years, no vaccine to combat the disease has been licenced for use in at risk populations. Here, we describe a novel recombinant vesicular stomatitis virus (rVSV) vector vaccine expressing modified HIV envelope glycoproteins and Ebola virus glycoprotein. Three heterologous immunizations successfully prevented infection by a different clade SHIV in 60% of non-human primates (NHPs). No trend was observed between resistance and antibody interactions. Resistance to infection was associated with high proportions of central memory T-cell CD69 and CD154 marker upregulation, increased IL-2 production, and a reduced IFN-γ response, offering insight into correlates of protection.

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)0.004
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: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.042
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0420.019

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 head0.035
GPT teacher head0.309
Teacher spread0.274 · 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
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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