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Record W3090443648 · doi:10.3390/vaccines8040569

Combination of Innate Immune Modulators as Vaccine Adjuvants in Mice

2020· article· en· W3090443648 on OpenAlexafffund
Azita Haddadi, Alyssa Chaffey, Siew Hon Ng, Yalamati Damayanthi, Heather L. Wilson

Bibliographic record

VenueVaccines · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsAlberta EnergyUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAdjuvantOvalbuminImmune systemMuramyl dipeptideTLR3ImmunologyImmunopotentiatorAntigenTLR2SplenocyteInnate immune systemBiologyToll-like receptor

Abstract

fetched live from OpenAlex

The development of new, effective, and safe vaccines necessarily requires the identification of new adjuvant(s) to enhance the potency and longevity of antigen-specific immune responses. In the present study, we compare the antibody-mediated and cell-mediated immune (CMI) responses within groups of mice vaccinated subcutaneously with ovalbumin (OVA; as an experimental antigen) plus polyphosphazene (an innate immune modulator), Polyinosinic:polycytidylic acid (poly-I:C; (an RNA mimetic) and glycopeptide ARC5 (which is a Toll-like receptor (TLR), TLR2 ligand and PAM3CSK4 analogue) formulated together in a soluble vaccine. We also investigated the effect of a polymeric nanoparticle of ARC4 and ARC7 (which are a novel muramyl dipeptide analogue and a monophosophoryl lipid A (MPLA) analogue, respectively) plus OVA +/- ARC5 as a subcutaneous vaccine in mice. OVA+ARC4/ARC7 nanoparticle +/- ARC5 triggered a robust and balanced Th1/Th2-type humoral response with significant anti-OVA IgA in serum, and significant interferon (IFN)-γ and interleukin (IL)-17 production in splenocytes after 35 days relative to the controls. Formulation of OVA with ARC4/ARC7 nanoparticles should be investigated for inducing protective immunity against infectious pathogens in mice and other species.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.147
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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 teacher head, 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

Citations8
Published2020
Admission routes2
Has abstractyes

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