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Record W3016228284 · doi:10.1016/j.omto.2020.03.018

Viral Delivery of CAR Targets to Solid Tumors Enables Effective Cell Therapy

2020· article· en· W3016228284 on OpenAlexaff
Amin Aalipour, Matthew Y. H. Tang, Surya Murty, Federico Simonetta, Alexander X. Lozano, Travis M. Shaffer, John C. Bell, Sanjiv S. Gambhir

Bibliographic record

VenueMolecular Therapy — Oncolytics · 2020
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversity of TorontoOttawa HospitalUniversity of Ottawa
FundersNational Cancer InstituteNational Institutes of HealthBen and Catherine Ivy FoundationCanary Foundation
KeywordsChimeric antigen receptorOncolytic virusAntigenCD19Cancer researchCell therapyMelanomaCellBiologyImmunotherapyImmunologyVirologyImmune systemTumor cells

Abstract

fetched live from OpenAlex

. In an immunocompetent model of B16 melanoma, this combination markedly delayed tumor growth and improved median survival compared with antigen-mismatched combinations. We also found that CD19 delivery could improve CAR T cell activity against tumor cells that express low levels of cognate antigen, suggesting a potential application in counteracting antigen-low escape. This approach highlights the potential of engineering tumors for effective adoptive cell therapy.

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 categoriesMeta-epidemiology (narrow)
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 score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.301
Teacher spread0.281 · 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.

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

Citations77
Published2020
Admission routes1
Has abstractyes

Explore more

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