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Record W7116727433 · doi:10.1038/s41392-025-02508-0

Working together: a multi-component intranasal vaccine provides synergistic protection against COVID-19

2025· article· en· W7116727433 on OpenAlexaff
Jessica A. Breznik, Chris P. Verschoor

Bibliographic record

VenueSignal Transduction and Targeted Therapy · 2025
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsHealth Sciences NorthUniversity of SudburyMcMaster UniversityUniversity of Guelph
Fundersnot available
KeywordsNasal administrationImmunityImmune systemAntibodyMEDLINEVaccination

Abstract

fetched live from OpenAlex

In their recent publication in Nature Biomedical Engineering , Hong and colleagues 1 report on the development of COVID-19 intranasal vaccines that combine adenoviral vector and protein subunit vaccine platforms. A two-component vaccine comprised of a human adenovirus expressing the full spike protein and a recombinant spike protein receptor binding domain generated mucosal and systemic immunity against live viral challenge while preventing transmission in animal models, and was shown to be well-tolerated, safe, and effective at inducing antibody responses in humans (Fig. 1 ). Fig. 1 A summary of the major experiments used to support the efficacy of the multi-component intranasal COVID-19 vaccine by Hong and colleagues Full size image

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 score0.318
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.056
GPT teacher head0.332
Teacher spread0.276 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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