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Record W2579975331 · doi:10.1016/j.vaccine.2019.02.059

A flow cytometric granularity assay for the quantification of infectious virus

2019· article· en· W2579975331 on OpenAlexafffund
Megan Logan, Julia Manalil, Christina Notte, Craig Kearse, Steve George, A. Zeiser, Paul J. Farrell, Marc G. Aucoin

Bibliographic record

VenueVaccine · 2019
Typearticle
Languageen
FieldMedicine
TopicHerpesvirus Infections and Treatments
Canadian institutionsSanofi (Canada)University of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVirologyTiterRecombinant DNAVirus quantificationVero cellFlow cytometryBiologyVirusGranularityHerpes simplex virusRecombinant virusMolecular biologyGene

Abstract

fetched live from OpenAlex

A flow cytometry-based assay was developed to assess the infective titer of two recombinant viruses: a recombinant herpes simplex type 2 (rHSV-2) and a recombinant canary pox (rALVAC.gfp). This method uses granularity of infected Vero and QT-35 cells, respectively, and correlates this to the infectious titer of virus samples. The percent of the cell populations with a high level of granularity could accurately be correlated to viral titers obtained through a traditional plaque assay, with R2 values greater than 0.8 using a semi-logarithmic scale. This approach offers a rapid, high-throughput method for infectious virus titration with similar accuracy to a traditional plaque assay.

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)0.003
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.022
GPT teacher head0.300
Teacher spread0.277 · 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

Citations7
Published2019
Admission routes2
Has abstractyes

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