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Record W2441193511 · doi:10.1007/978-1-61779-340-0_12

Exploring Host Factors that Impact Reovirus Replication, Dissemination, and Reovirus-Induced Cell Death in Cancer Versus Normal Cells in Culture

2011· article· en· W2441193511 on OpenAlexaff
Maya Shmulevitz, Patrick W.K. Lee

Bibliographic record

VenueMethods in molecular biology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsDalhousie UniversityUniversity of Alberta
Fundersnot available
KeywordsOncolytic virusVirologyBiologyCell cultureVirusViral replicationInterferonCancer cellCellApoptosisTransformation (genetics)CancerGeneGenetics

Abstract

fetched live from OpenAlex

Oncolytic viruses, such as reovirus, offer a promising approach to cancer treatment. Concurrently, oncolytic viruses provide a valuable tool for deciphering unique attributes of cancer cells that support superior virus replication, cell death, or virus dissemination. Through our studies on various cancer cell lines, as well as isogenic cells with and without transformation by oncogenic Ras, we have identified at least four steps of virus replication that can be augmented in transformed cells. Ras transformation can support efficient reovirus uncoating during entry, production of progeny with high infectious capacity, and reovirus-induced apoptosis. Furthermore, Ras transformation also precludes interferon production following reovirus infection, permitting enhanced cell-to-cell virus spread. Methods that measure the efficiency of reovirus replication and dissemination described in this chapter can be used in combination with assorted cell culture systems to better understand the host factors that regulate reovirus oncolysis.

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 score0.001
Threshold uncertainty score0.004

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 communication0.0010.000
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 head0.155
GPT teacher head0.447
Teacher spread0.293 · 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

Citations23
Published2011
Admission routes1
Has abstractyes

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