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Record W4281789576 · doi:10.3389/fimmu.2022.860399

Differential Biodistribution of Adenoviral-Vectored Vaccine Following Intranasal and Endotracheal Deliveries Leads to Different Immune Outcomes

2022· article· en· W4281789576 on OpenAlexafffund
Vidthiya Jeyanathan, Sam Afkhami, Michael R. D’Agostino, Anna Zganiacz, Xueya Feng, Matthew S. Miller, Mangalakumari Jeyanathan, Michael R. Thompson, Zhou Xing

Bibliographic record

VenueFrontiers in Immunology · 2022
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsImmunogenicityBiodistributionNasal administrationMedicineRespiratory tractImmune systemImmunologyRespiratory systemInternal medicineBiologyIn vivo

Abstract

fetched live from OpenAlex

Infectious diseases of the respiratory tract are one of the top causes of global morbidity and mortality with lower respiratory tract infections being the fourth leading cause of death. The respiratory mucosal (RM) route of vaccine delivery represents a promising strategy against respiratory infections. Although both intranasal and inhaled aerosol methods have been established for human application, there is a considerable knowledge gap in the relationship of vaccine biodistribution to immune efficacy in the lung. Here, by using a murine model and an adenovirus-vectored model vaccine, we have compared the intranasal and endotracheal delivery methods in their biodistribution, immunogenicity and protective efficacy. We find that compared to intranasal delivery, the deepened and widened biodistribution in the lung following endotracheal delivery is associated with much improved vaccine-mediated immunogenicity and protection against the target pathogen. Our findings thus support further development of inhaled aerosol delivery of vaccines over intranasal delivery for human application.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.008
GPT teacher head0.237
Teacher spread0.229 · 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 designObservational
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

Citations26
Published2022
Admission routes2
Has abstractyes

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