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Record W1533777667 · doi:10.1111/1348-0421.12238

Development and characterization of neutralizing monoclonal antibodies against canine distemper virus hemagglutinin protein

2015· article· en· W1533777667 on OpenAlexaff
Zhenwei Bi, Xingxia Xia, Yongshan Wang, Yongjie Mei

Bibliographic record

VenueMicrobiology and Immunology · 2015
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsMinistry of Agriculture
Fundersnot available
KeywordsCanine distemperVirologyBiologyEpitopeVirusMonoclonal antibodyMorbillivirusImmunofluorescenceHemagglutinin (influenza)AntibodyNeutralizationAntigenParamyxoviridaeNeutralizing antibodyBlotEpitope mappingViral diseaseImmunology

Abstract

fetched live from OpenAlex

Canine distemper virus (CDV) causes a serious multisystemic disease in dogs and other carnivora. Hemagglutinin (H) protein-specific antibodies are mainly responsible for protective immunity against CDV infection. In the present study, six neutralizing MAbs to the H protein of CDV were newly obtained and characterized by immunizing BALB/c mice with a recent Chinese field isolate. Competitive binding inhibition assay revealed that they recognized four distinct antigenic regions of the H protein. Immunofluorescence assay and western blotting showed that all MAbs recognize the conformational rather than the linear epitopes of the H protein. Furthermore, in immunofluorescence and virus neutralization assays, two of the MAbs were found to react only with the recent Chinese field isolate and not with older CDV strains, including vaccine strain Onderstepoort, indicating there are neutralization-related antigenic variations between the recent Chinese field isolate and the older CDV strains examined in this study. The newly established MAbs are useful for differentiating the expanding CDV strains and could be used in immunotherapy and immunodiagnosis against infection with CDV.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score0.417

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 studies0.0000.000
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 head0.023
GPT teacher head0.246
Teacher spread0.223 · 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 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

Citations22
Published2015
Admission routes1
Has abstractyes

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