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Record W2032502166 · doi:10.1080/15321810701454706

Development of a Competitive Enzyme Linked Immunosorbent Assay to Identify Epitope Specific Antibodies in Recipients of the U.S. Licensed Anthrax Vaccine

2007· article· en· W2032502166 on OpenAlexaff
Michael J. Gubbins, Lisa Schmidt, Raymond S. W. Tsang, Jody D. Berry, Amin Kabani, Donald I.H. Stewart

Bibliographic record

VenueJournal of Immunoassay and Immunochemistry · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacillus and Francisella bacterial research
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsEpitopeVirologyAnthrax vaccinesAntibodyBiologyImmunologyImmunization

Abstract

fetched live from OpenAlex

Vaccination with anthrax vaccine adsorbed (AVA) results in the production of protective antigen (PA) specific antibodies, which play an important protective role against anthrax toxins. Analyzing the specificity of serum antibodies generated in response to AVA vaccination can provide insight into the mechanisms of protective immunity against this important pathogen. The goal of this study was to develop a competitive enzyme linked immunosorbent assay (cELISA) to test human immune serum for antibodies specific for a known lethal toxin neutralizing epitope in PA. PA-specific antibodies in sera from individuals who received the six-dose AVA vaccine series competed for binding to immobilized PA with monoclonal antibody F20G75, which binds to a linear epitope in domain 2 of PA and neutralizes lethal toxin activity in vitro. These results suggest that antibodies in human AVA vaccinee serum recognize the same epitope as F20G75, or one in close proximity to it, and may serve a protective role against anthrax lethal toxin. This assay may be used for serological confirmation of successful immunization against anthrax and for the identification of antibodies in human vaccinee serum that recognize protective epitopes on PA.

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)0.002
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 score0.200
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.014
GPT teacher head0.294
Teacher spread0.281 · 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

Citations11
Published2007
Admission routes1
Has abstractyes

Explore more

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