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Record W2056407096 · doi:10.1089/vim.2011.0085

Vaccination with CpG-Adjuvanted Avian Influenza Virosomes Promotes Antiviral Immune Responses and Reduces Virus Shedding in Chickens

2012· article· en· W2056407096 on OpenAlexafffund
Amirul Islam Mallick, Raveendra R. Kulkarni, Michael St. Paul, Payvand Parvizi, Éva Nagy, Shahriar Behboudi, Shayan Sharif

Bibliographic record

VenueViral Immunology · 2012
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Poultry Research Council
KeywordsVirologyVirusBiologyImmune systemViral sheddingAntibodyAdjuvantHemagglutination assayCpG OligodeoxynucleotideInterferonImmunologyTiterGeneGene expression

Abstract

fetched live from OpenAlex

The use of virosomes as a vaccine platform has proven successful against several viruses. Here we examined the protective efficacy of a virosome-based vaccine consisting of avian influenza virus (AIV) A/Duck/Czech/56/H4N6 in chickens against a homologous AIV challenge. Virosomes adjuvanted with CpG-ODN or recombinant chicken interferon (IFN)-γ significantly reduced virus shedding after virus challenge. Furthermore, immunization with virosomes adjuvanted with CpG-ODN increased hemagglutination inhibition (HI) and virus-specific neutralizing serum antibodies, as well as virus-specific serum IgG and mucosal IgA responses. We also found a significant increase in the expression of type I and II interferon genes in the protected birds following virus challenge. In summary, this study demonstrated the ability of virosomes adjuvanted with CpG-ODN to reduce AIV shedding, and elicit virus-specific protective antibody responses in vaccinated birds.

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 score0.001
Threshold uncertainty score0.002

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 integrity0.0000.000
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 head0.042
GPT teacher head0.355
Teacher spread0.313 · 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

Citations21
Published2012
Admission routes2
Has abstractyes

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