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Record W2980170727 · doi:10.4167/jbv.2019.49.3.124

Recharacterization of the Canine Adenovirus Type 1 Vaccine Strain based on the Biological and Molecular Properties

2019· article· en· W2980170727 on OpenAlexaboutno aff
Dong-Kun Yang, Ha-Hyun Kim, Eunjin Lee, Soon‐Seek Yoon, Jung-Won Park, Chae-Hyun Kim, Ho-Ryoung Kim

Bibliographic record

VenueJournal of Bacteriology and Virology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsnot available
Fundersnot available
KeywordsStrain (injury)VirologyHemagglutinationImmunofluorescenceBiologyCytopathic effectMolecular biologyMicrobiologyVirusAntibodyImmunology

Abstract

fetched live from OpenAlex

Canine adenovirus type 1 (CAV-1) infection results in hepatitis in dogs. In this study, we investigated the biologic and genetic characteristics of the CAV-1 vaccine strain (CAV1V) to improve quality control about CAV vaccine. The identity of CAV1V as CAV-1 was confirmed based on its cytopathic effects and the results of hemagglutination (HA) and immunofluorescence assays, and electron microscopy. The CAV1V strain reached 107.5 TCID50/mL in MDCK cells at 4 days post-inoculation and exhibited hemmagglutination activity of 256 U using guinea pig erythrocytes. Intranuclear fluorescence in the infected cells was observed and typical adenoviruses were observed in electon microscope. CAV1V strain was identified as a CAV-1 strain by nucleotide sequence analysis. In a comparison of the nucleotide sequences of the fiber genes of several CAV strains, CAV1V showed the highest similarity (99.8%) with the GLAXO strain, which was isolated in Canada. Our biological characterization of CAV1V will facilitate quality control of the canine hepatitis vaccine.

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 score0.003

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.256
Teacher spread0.236 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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