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Recognition and neutralization of bovine herpesvirus-1 by bovine antibody variable heavy- and light-chain domains (P4182)

2013· article· en· W4313386317 on OpenAlexaffabout
Yfke Pasman, Éva Nagy, Azad Kasuhik

Bibliographic record

VenueThe Journal of Immunology · 2013
Typearticle
Languageen
FieldMedicine
TopicHerpesvirus Infections and Treatments
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsNeutralizationPichia pastorisImmunoglobulin light chainAntibodyVirologyBovine herpesvirus 1In vitroNeutralizing antibodyMolecular biologyAntigenChemistryBiologyRecombinant DNAGeneVirusHerpesviridaeBiochemistryImmunologyViral disease

Abstract

fetched live from OpenAlex

Abstract Mono- and multimeric single chain variable fragments (scFvs), expressed in Pichia pastoris, are capable of neutralizing Bovine Herpesvirus type-1 (BoHV-1) in vitro [Koti et al. 2011. Vaccine 29; Pasman et al., 2012. Clin Vaccine Immunol 19]. The cDNA encoding these scFvs originated from a mouse x cattle hybridoma that secreted bovine IgG1 capable of neutralizing BoHV-1. The molecular modelling of heavy (VH) and light (VL) chain domains predicted that the VL might only have a supportive role in antigen recognition. VH and VL were expressed as single domains (Fd), in P. pastoris with an objective to assess their role in viral recognition and neutralization. The ability to recognize and neutralize BoHV-1 of the monomeric scFv3-18L, VH and VL linked via 18 amino acid linker, was compared to the ability of the individual FdVH and FdVL domains, in an ELISA and in vitro plaque reduction assays. These experiments demonstrated that while FdVH, but not FdVL, recognized BoHV-1 in an ELISA, FdVH did not neutralize BoHV-1. The complexities associated with such antibody functions will be discussed. [Supported by NSERC Canada]

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)0.0010.000

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 head0.253
Teacher spread0.241 · 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

Citations0
Published2013
Admission routes2
Has abstractyes

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