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Record W2464815631 · doi:10.3390/ijms17071062

Mechanisms Underlying the Immune Response Generated by an Oral Vibrio cholerae Vaccine

2016· article· en· W2464815631 on OpenAlexafffund
Danylo Sirskyj, Ashok Kumar, Ali Azizi

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVibrio bacteria research studies
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsImmune systemVibrio choleraeTRIFImmunologyBiologyImmunizationCholera toxinSignal transductionCholera vaccineToll-like receptorAntibodyCell biologyMicrobiologyInnate immune systemBacteria

Abstract

fetched live from OpenAlex

Mechanistic details underlying the resulting protective immune response generated by mucosal vaccines remain largely unknown. We investigated the involvement of Toll-like receptor signaling in the induction of humoral immune responses following oral immunization with Dukoral, comparing wild type mice with TLR-2-, TLR-4-, MyD88- and Trif-deficient mice. Although all groups generated similar levels of IgG antibodies, the proliferation of CD4+ T-cells in response to V. cholerae was shown to be mediated via MyD88/TLR signaling, and independently of Trif signaling. The results demonstrate differential requirements for generation of immune responses. These results also suggest that TLR pathways may be modulators of the quality of immune response elicited by the Dukoral vaccine. Determining the critical signaling pathways involved in the induction of immune response to this vaccine would be beneficial, and could contribute to more precisely-designed versions of other oral vaccines in the future.

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 score0.003

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.338
Teacher spread0.308 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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