MétaCan
Menu
Back to cohort
Record W1578622542 · doi:10.4049/jimmunol.180.12.8461

Novel Vaccination for Allergy through Gene Silencing of CD40 Using Small Interfering RNA

2008· article· en· W1578622542 on OpenAlexaff
Motohiko Suzuki, Xiufen Zheng, Xusheng Zhang, Mu Li, Costin Vladau, Thomas E. Ichim, Hongtao Sun, Bertha García, Wei‐Ping Min

Bibliographic record

VenueThe Journal of Immunology · 2008
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity HospitalLawson Health Research InstituteWestern University
Fundersnot available
KeywordsGene silencingSmall interfering RNARNA interferenceImmune systemImmunologyAllergyCD40BiologyGeneRNACytotoxic T cellIn vitroGenetics

Abstract

fetched live from OpenAlex

Small interfering RNA (siRNA) is a potent means of inducing gene-specific silencing. Gene silencing strategies using siRNA have demonstrated therapeutic benefits in animal models of various diseases, and are currently in clinical trials. However, the utility of gene silencing as a treatment for allergic diseases has not yet been reported. In this study, we report a novel therapy for allergy through gene silencing of CD40, a critical costimulatory molecule and a key factor in allergic immune responses. Silencing CD40 resulted in generation of immunoregulatory dendritic cells (DCs). Administration of CD40 siRNA remarkably reduced nasal allergic symptoms and local eosinophil accumulation in the OVA-induced allergic mice. The OVA-specific T cell response was inhibited after the CD40 siRNA treatment. Additionally, anti-OVA specific IgE and production of IL-4 and IL-5 of T cells stimulated by OVA were significantly decreased in CD40 siRNA-treated mice. Furthermore, we demonstrated that the therapeutic effects by CD40 siRNA were associated with impaired Ag-presenting functions of DCs and B cells, and generation of regulatory T cells. The present study highlights a therapeutic potential of siRNA-based treatment for allergic diseases.

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)0.001
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 score0.094
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.050
GPT teacher head0.268
Teacher spread0.217 · 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

Citations31
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of ImmunologySame topicImmunotherapy and Immune ResponsesFrench-language works237,207