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Record W3196695280 · doi:10.1016/j.omtm.2021.08.004

Production and purification of high-titer OrfV for preclinical studies in vaccinology and cancer therapy

2021· article· en· W3196695280 on OpenAlexafffund
Jacob P. van Vloten, Jessica A. Minott, Thomas M. McAusland, Joelle C. Ingrao, Lisa A. Santry, Grant McFadden, Jim Petrik, Byram W. Bridle, Sarah K. Wootton

Bibliographic record

VenueMolecular Therapy — Methods & Clinical Development · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaCancer Research Society
KeywordsTiterCancerVirologyCancer therapyMedicineBiologyInternal medicine

Abstract

fetched live from OpenAlex

Poxviruses have been used extensively as vaccine vectors for human and veterinary medicine and have recently entered the clinical realm as immunotherapies for cancer. We present a comprehensive method for producing high-quality lots of the poxvirus Parapoxvirus ovis (OrfV) for use in preclinical models of vaccinology and cancer therapy. OrfV is produced using a permissive sheep skin-derived cell line and is released from infected cells by repeated freeze-thaw combined with sonication. We present two methods for isolation and purification of bulk virus. Isolated virus is concentrated to high titer using polyethylene glycol to produce the final in vivo -grade product. We also describe methods for quantifying OrfV infectious virions and determining genomic copy number to evaluate virus stocks. The methods herein will provide researchers with the ability to produce high-quality, high-titer OrfV for use in preclinical studies, and support the translation of OrfV-derived technologies into the clinic.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.219
GPT teacher head0.529
Teacher spread0.310 · 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
GenreMethods

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

Citations13
Published2021
Admission routes2
Has abstractyes

Explore more

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