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Record W1484583306 · doi:10.1111/vco.12124

The effects of oncolytic reovirus in canine lymphoma cell lines

2014· article· en· W1484583306 on OpenAlexaff
Chung Chew Hwang, Saori Umeki, Masaya Igase, Matthew Coffey, Shunsuke Noguchi, Masaru Okuda, Takuya Mizuno

Bibliographic record

VenueVeterinary and Comparative Oncology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsOncolytics Biotech (Canada)
FundersVeterinärmedizinische Universität WienUniversity of TokyoUniversiti Putra MalaysiaOhio State University
KeywordsOncolytic virusCanine LymphomaPropidium iodideLymphomaCell cultureVirologyApoptosisProgrammed cell deathBiologyVirusCanine distemperBurkitt's lymphomaCancer researchImmunology

Abstract

fetched live from OpenAlex

Reovirus is a potent oncolytic virus in many human neoplasms that has reached phase II and III clinical trials. Our laboratory has previously reported the oncolytic effects of reovirus in canine mast cell tumour (MCT). In order to further explore the potential of reovirus in veterinary oncology, we tested the susceptibility of reovirus in 10 canine lymphoma cell lines. Reovirus-induced cell death, virus replication and infectivity were confirmed in four cell lines with variable levels of susceptibility. The level of Ras activation varied among the cell lines with no correlation with reovirus susceptibility. Reovirus-susceptible cell lines underwent apoptosis as proven by propidium iodide (PI) staining, Annexin V-FITC/PI assay, cleavage of PARP and inhibition of cell death by caspase inhibitor. A single intratumoral injection of reovirus suppressed the growth of canine lymphoma subcutaneous tumour in NOD/SCID mice. Unlike canine MCT, canine lymphoma is less susceptible to reovirus.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.341
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2014
Admission routes1
Has abstractyes

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