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Record W2986507273 · doi:10.1080/22221751.2019.1683436

Antigenic variations of recent street rabies virus

2019· article· en· W2986507273 on OpenAlexaff
Wenbo Wang, Jian Ma, Jianhui Nie, Jia Li, Shouchun Cao, Lan Wang, Chuanfei Yu, Weijin Huang, Yuhua Li, Yongxin Yu, Mifang Liang, Brett Zirkle, Xiaojiang S. Chen, Xuguang Li, Wei Kong, Youchun Wang

Bibliographic record

VenueEmerging Microbes & Infections · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicRabies epidemiology and control
Canadian institutionsHealth Canada
FundersMinistry of Science and Technology of the People's Republic of China
KeywordsVirologyRabies virusBiologyAntigenic variationRabiesAntigenVirusMonoclonal antibodyAntibodyGenetics

Abstract

fetched live from OpenAlex

The genetic and/or antigenic differences between street rabies virus (RABV) and vaccine strains could potentially affect effectiveness of rabies vaccines. As such, it is important to continue monitoring the glycoprotein (G) of the street isolates. All RABVG sequences in public database were retrieved and analysed. Using a pseudovirus system, we investigated 99 naturally occurring mutants for their reactivities to well-characterized neutralizing monoclonal antibodies (mAbs) and vaccine-induced antisera. A divergence in G sequences was found between vaccine strains and recent street isolates, with mutants demonstrating resistance to neutralizing mAbs and vaccine-induced antibodies. Moreover, antigenic variants were observed in a wide range of animal hosts and geographic locations, with most of them emerging since 2010. As the number of antigenic variants has increased in recent years, close monitoring on street isolates should be strengthened.

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: Observational · Consensus signal: none
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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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 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

Citations28
Published2019
Admission routes1
Has abstractyes

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