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Record W2778853333 · doi:10.1097/cji.0000000000000208

Brief Communication; A Heterologous Oncolytic Bacteria-Virus Prime-Boost Approach for Anticancer Vaccination in Mice

2017· article· en· W2778853333 on OpenAlex
Amelia S. Aitken, Dominic G. Roy, Nikolas T. Martin, Subash Sad, John C. Bell, Marie‐Claude Bourgeois‐Daigneault

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

VenueJournal of Immunotherapy · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsOncolytic virusHeterologousVaccinationPriming (agriculture)VirologyVirusImmune systemBiologyAntigenAdjuvantImmunology

Abstract

fetched live from OpenAlex

Anticancer vaccination is becoming a popular therapeutic approach for patients with cancers expressing common tumor antigens. One variation on this strategy is a heterologous virus vaccine where 2 viruses encoding the same tumor antigen are administered sequentially to prime and boost antitumor immunity. This approach is currently undergoing clinical investigation using an adenovirus (Ad) and the oncolytic virus Maraba (MRB). In this study, we show that Listeria monocytogenes can be used in place of the Ad to obtain comparable immune priming efficiency before MRB boosting. Importantly, the therapeutic benefits provided by our heterologous L. monocytogenes-MRB prime-boost strategy are superior to those conferred by the Ad-MRB combination. Our study provides proof of concept for the heterologous oncolytic bacteria-virus prime-boost approach for anticancer vaccination and merits its consideration for clinical testing.

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)0.001
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 score0.045
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.025
GPT teacher head0.339
Teacher spread0.314 · 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