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Record W3002937642 · doi:10.1111/tbed.13494

Morphological features and pathogenicity of mutated canine influenza viruses from China and South Korea

2020· article· en· W3002937642 on OpenAlexaff
Woonsung Na, Xing Xie, Minjoo Yeom, Aram Kang, Hyun‐Ouk Kim, Jongwoo Lim, Geunseon Park, Sun‐Woo Yoon, Dae Gwin Jeong, Hye Kwon Kim, Seungjoo Haam, Yongjie Liu, Daesub Song

Bibliographic record

VenueTransboundary and Emerging Diseases · 2020
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMinistry of Agriculture
FundersKorea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries
KeywordsVirulenceVirologyOutbreakVirusPathogenesisPathogenicityBiologyMutantMorphology (biology)PandemicTransmission (telecommunications)Host (biology)Coronavirus disease 2019 (COVID-19)MicrobiologyZoologyGeneImmunologyGeneticsDiseaseMedicinePathology

Abstract

fetched live from OpenAlex

The canine influenza virus (CIV) has spread globally from East Asia to the United States and mutated and evolved to generate various CIVs. Since 2010, the mutant CIVs found in China and Korea have presented increased virulence in mice, guinea pigs and ferrets, which has raised concerns about public health and outbreak of a severe canine flu. We analysed and compared the morphology, cellular uptake and pathogenicity of CIV variants in host animals, to determine their characteristics. The Chinese mutant, A/canine/Jiangsu/06/2010[H3N2](JS10), has two amino acid insertions at the distal end of the NA stalk, and A/canine/Korea/01/2007[H3N2](KR07) presented comparable efficiency of cell uptake and a similar morphology to spherical or small ovoid particles. However, KR07M generated from swapping of M segment of the pandemic isolate, A/California/04/2009 [H1N1] (CA04) into KR07 alone accounted for morphologic change and higher efficiency of cell uptake to the wild-type CIV. This study will provide an insight into the pathogenesis, transmission and evolution of CIVs and help determine future countermeasures.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.054
GPT teacher head0.325
Teacher spread0.271 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2020
Admission routes1
Has abstractyes

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