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Record W2022690205 · doi:10.1051/vetres:2006012

Mucosal delivery of vaccines in domestic animals

2006· review· en· W2022690205 on OpenAlexafffund
Volker Gerdts, George Mutwiri, Suresh K. Tikoo, Lorne A. Babiuk

Bibliographic record

VenueVeterinary Research · 2006
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaSaskatchewan Health Research Foundation
KeywordsMucosal immunityVaccinationImmunizationImmunologyImmune systemImmunityBiologyMucosal immunologyVirology

Abstract

fetched live from OpenAlex

Mucosal vaccination is proving to be one of the greatest challenges in modern vaccine development. Although highly beneficial for achieving protective immunity, the induction of mucosal immunity, especially in the gastro-intestinal tract, still remains a difficult task. As a result, only very few mucosal vaccines are commercially available for domestic animals. Here, we critically review various strategies for mucosal delivery of vaccines in domestic animals. This includes live bacterial and viral vectors, particulate delivery-systems such as polymers, alginate, polyphosphazenes, immune stimulating complex and liposomes, and receptor mediated-targeting strategies to the mucosal tissues. The most commonly used routes of immunization, strategies for delivering the antigen to the mucosal surfaces, and future prospects in the development of mucosal vaccines are discussed.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.186
GPT teacher head0.452
Teacher spread0.266 · 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
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

Citations82
Published2006
Admission routes2
Has abstractyes

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