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Record W2100898084 · doi:10.1002/jmv.21381

Identification of genetic mutations associated with attenuation and changes in tropism of Urabe mumps virus

2008· article· en· W2100898084 on OpenAlexafffund
Dion Shah, Silvia M. Vidal, Malen A. Link, Steven A. Rubin, Kathryn E. Wright

Bibliographic record

VenueJournal of Medical Virology · 2008
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsMcGill UniversityMcGill Genome CentreUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaOak Ridge Institute for Science and EducationU.S. Food and Drug AdministrationU.S. Department of Energy
KeywordsVirologyMumps virusMumps vaccineBiologyTropismVirusVaccinationVirulenceParamyxoviridaePoliovirusMicrobiologyViral diseaseGeneGeneticsRubellaMeasles

Abstract

fetched live from OpenAlex

Although several effective mumps virus vaccines have been developed, almost nothing is known about the genetic changes responsible for loss of virulence. One vaccine, Urabe AM9, was withdrawn from the market because of insufficient attenuation. The vaccine was found to contain a mixture of viruses that could be distinguished based on the sequence of the hemagglutinin-neuraminidase gene (HN). Viruses containing lysine at HN amino acid position 335 were isolated from cases of post-vaccination parotitis or meningitis whereas viruses containing glutamic acid at this position were not associated with post-vaccination disease. Using a rat based model of mumps neurovirulence, we demonstrate that this latter virus is significantly attenuated compared to a virus isolated from a patient with post-vaccination meningitis. Complete sequence analysis of the genomes of the two viruses identified sixteen genetic differences, some or all of which must be responsible for differences in virulence. These same genetic differences also account for changes in tropism in cell culture.

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 score0.002

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)0.0010.000

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 head0.022
GPT teacher head0.295
Teacher spread0.273 · 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

Citations17
Published2008
Admission routes2
Has abstractyes

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