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Record W3027487440 · doi:10.3390/v12050579

Zika Virus with Increased CpG Dinucleotide Frequencies Shows Oncolytic Activity in Glioblastoma Stem Cells

2020· article· en· W3027487440 on OpenAlexafffund
Ivan Trus, Nathalie G. Bérubé, Peng Jiang, Janusz Rak, Volker Gerdts, Uladzimir Karniychuk

Bibliographic record

VenueViruses · 2020
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsMcGill University Health CentreUniversity of Saskatchewan
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Neurological Disorders and StrokeSaskatchewan Health Research Foundation
KeywordsOncolytic virusGlioblastomaZika virusCpG siteStem cellBiologyVirologyCancer researchVirusGeneDNA methylationGeneticsGene expression

Abstract

fetched live from OpenAlex

We studied whether cytosine phosphate-guanine (CpG) recoding in a viral genome may provide oncolytic candidates with reduced infection kinetics in nonmalignant brain cells, but with high virulence in glioblastoma stem cells (GSCs). As a model, we used well-characterized CpG-recoded Zika virus vaccine candidates that previously showed genetic stability and safety in animal models. In vitro, one of the CpG-recoded Zika virus variants had reduced infection kinetics in nonmalignant brain cells but high infectivity and oncolytic activity in GSCs as represented by reduced cell proliferation. The recoded virus also efficiently replicated in GSC-derived tumors in ovo with a significant reduction of tumor growth. We also showed that some GSCs may be resistant to Zika virus oncolytic activity, emphasizing the need for personalized oncolytic therapy or a strategy to overcome resistance in GSCs. Collectively, we demonstrated the potential of the CpG recoding approach for oncolytic virus development that encourages further research towards a better understanding of host-tumor-CpG-recoded virus interactions.

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 score0.029
Threshold uncertainty score0.904

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)0.0010.000

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 head0.026
GPT teacher head0.253
Teacher spread0.227 · 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

Citations38
Published2020
Admission routes2
Has abstractyes

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