MétaCan
Menu
← Back to cohort

The VSVΔG/ZEBOV GP vaccine protects against an Ebola infection and induces humoral and cellular immunity (128.16)

2009· article· en· W4313344597 on OpenAlexaff
Judie B. Alimonti, Xiangguo Qiu, Lisa Fernando, Leno Melito, Friedericke Feldmann, Ute Ströher, Heinz Feldmann, Steven J.M. Jones

Bibliographic record

VenueThe Journal of Immunology · 2009
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
Fundersnot available
KeywordsVirologyEbola vaccineVesicular stomatitis virusImmune systemEbola virusImmunizationImmunologyEbolavirusHumoral immunityBiologyImmunityVesicular StomatitisVirus

Abstract

fetched live from OpenAlex

Abstract Ebola virus (EBOV) is a highly infectious filovirus causing a severe hemorrhagic fever in humans with fatality rates approaching 90%. We used the live attenuated recombinant vaccine vector vesicular stomatitis virus (VSV) to create a vaccine for EBOV. Ten cynomolgus macaques were immunized either intramuscularly (IM), orally (OR), or intranasally (IN) with VSVΔG/ZEBOVGP 28 days before receiving a lethal challenge of Zaire ebolavirus (ZEBOV). There were no signs of illness in the vaccinated animals, and no loss of lymphocytes typically seen during infection. ZEBOVGP-specific humoral and cell mediated immune responses (CMIR) were generated by all immunization routes. Surprisingly, in general, the IN route generated more potent humoral responses, while the IM route generated early IL-2 and IFN-γ CMIR. We demonstrate that a single dose of the VSVΔG/ZEBOVGP vaccine delivered by any of the three routes induces cellular and humoral immune responses and protects non-human primates against a lethal challenge of ZEBOV.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.022
GPT teacher head0.304
Teacher spread0.282 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Immunology→Same topicViral Infections and Outbreaks Research→French-language works237,207→