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TLR9<sup>−/−</sup> and TLR9<sup>+/+</sup> mice display similar immune responses to a DNA vaccine

2004· article· en· W1972351486 on OpenAlexaff
Shawn Babiuk, Neeloffer Mookherjee, Reno Pontarollo, Phillip Griebel, Sylvia van Drunen Littel‐van den Hurk, Rolf Hecker, Lorne A. Babiuk

Bibliographic record

VenueImmunology · 2004
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsUniversity of SaskatchewanUniversity of British ColumbiaThe Metabolomics Innovation Centre
Fundersnot available
KeywordsTLR9Toll-Like Receptor 9ImmunogenicityImmune systemDNA vaccinationCpG siteBiologyAdjuvantAntigenImmunizationPlasmidImmunityImmunologyDNAGeneGeneticsDNA methylation

Abstract

fetched live from OpenAlex

Plasmid DNA continues to attract interest as a potential vaccine-delivery vehicle. However, the mechanisms whereby immune responses are elicited by plasmids are not fully understood. Although there have been suggestions regarding the importance of CpG motifs in plasmid immunogenicity, the molecular mechanisms by which CpG motifs enhance immune responses to DNA vaccines are not well understood. As Toll-like receptor 9-deficient (TLR9-/-) mice fail to respond to the adjuvant effects of CpG oligonucleotides, we used these mice to determine the effect of CpG motifs in plasmids used for DNA immunization. In the study described below, we report that DNA immunization was as effective in eliciting antigen-specific antibody and at stimulating antigen-specific interferon-gamma (IFN-gamma)-secreting cells in TLR9-/- mice as in TLR9+/+ mice. This study illustrates that DNA vaccines elicit immune responses by multiple mechanisms and demonstrates that TLR9 is not essential for the induction of immune responses following DNA immunization.

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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.007
GPT teacher head0.234
Teacher spread0.226 · 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

Citations96
Published2004
Admission routes1
Has abstractyes

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