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Record W2345080518 · doi:10.1038/mtm.2016.30

Methods and clinical development of adenovirus-vectored vaccines against mucosal pathogens

2016· review· en· W2345080518 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueMolecular Therapy — Methods & Clinical Development · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
FundersGovernment of the United KingdomGovernment of OntarioMcMaster University
KeywordsVirologyViral vectorVaccinationImmunizationVector (molecular biology)Tuberculosis vaccinesVacciniaBiologyMycobacterium tuberculosisImmunologyMedicineImmune systemTuberculosisRecombinant DNA

Abstract

fetched live from OpenAlex

Adenoviruses represent the most widely used viral-vectored platform for vaccine design, showing a great potential in the fight against intracellular infectious diseases to which either there is a lack of effective vaccines or the traditional vaccination strategy is suboptimal. The extensive understanding of the molecular biology of adenoviruses has made the new technologies and reagents available to efficient generation of adenoviral-vectored vaccines for both preclinical and clinical evaluation. The novel adenoviral vectors including nonhuman adenoviral vectors have emerged to be the further improved vectors for vaccine design. In this review, we discuss the latest adenoviral technologies and their utilization in vaccine development. We particularly focus on the application of adenoviral-vectored vaccines in mucosal immunization strategies against mucosal pathogens including Mycobacterium tuberculosis, flu virus, and human immunodeficiency virus.

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.

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)0.024
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0020.001
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 head0.153
GPT teacher head0.535
Teacher spread0.382 · 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