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Record W4321500321 · doi:10.3390/ph16030334

Recent Advances in the Development of Adenovirus-Vectored Vaccines for Parasitic Infections

2023· review· en· W4321500321 on OpenAlexafffund
Cal Koger-Pease, Dilhan J. Perera, Momar Ndao

Bibliographic record

VenuePharmaceuticals · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersR. Howard Webster FoundationInstitut de recherche, Centre universitaire de santé McGill
KeywordsParasitic diseaseVirologyImmunologyMalariaDiseaseBiologyImmune systemToxoplasmosisImmunityVector (molecular biology)TuberculosisLeishmaniasisMedicine

Abstract

fetched live from OpenAlex

Vaccines against parasites have lagged centuries behind those against viral and bacterial infections, despite the devastating morbidity and widespread effects of parasitic diseases across the globe. One of the greatest hurdles to parasite vaccine development has been the lack of vaccine strategies able to elicit the complex and multifaceted immune responses needed to abrogate parasitic persistence. Viral vectors, especially adenovirus (AdV) vectors, have emerged as a potential solution for complex disease targets, including HIV, tuberculosis, and parasitic diseases, to name a few. AdVs are highly immunogenic and are uniquely able to drive CD8+ T cell responses, which are known to be correlates of immunity in infections with most protozoan and some helminthic parasites. This review presents recent developments in AdV-vectored vaccines targeting five major human parasitic diseases: malaria, Chagas disease, schistosomiasis, leishmaniasis, and toxoplasmosis. Many AdV-vectored vaccines have been developed for these diseases, utilizing a wide variety of vectors, antigens, and modes of delivery. AdV-vectored vaccines are a promising approach for the historically challenging target of human parasitic 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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.786

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 science0.0000.000
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 head0.188
GPT teacher head0.508
Teacher spread0.321 · 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 designOther design
Domainnot available
GenreReview

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

Citations17
Published2023
Admission routes2
Has abstractyes

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