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Record W2167410287 · doi:10.1128/cvi.05714-11

Analysis of Defined Combinations of Monoclonal Antibodies in Anthrax Toxin Neutralization Assays and Their Synergistic Action

2012· article· en· W2167410287 on OpenAlexaff
Miriam M. Ngundi, Bruce D. Meade, Stephen F. Little, Conrad P. Quinn, Cindi R. Corbett, Rebecca A. Brady, Drusilla Burns

Bibliographic record

VenueClinical and Vaccine Immunology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacillus and Francisella bacterial research
Canadian institutionsPublic Health Agency of Canada
FundersNational Institutes of Health
KeywordsBacillus anthracisAnthrax toxinAnthrax vaccinesAntibodyMonoclonal antibodyNeutralizationToxinVirologyAntigenBivalent (engine)MicrobiologyChemistryBiologyImmunologyRecombinant DNAImmunizationBacteriaBiochemistryFusion protein

Abstract

fetched live from OpenAlex

Antibodies against the protective antigen (PA) component of anthrax toxin play an important role in protection against disease caused by Bacillus anthracis. In this study, we examined defined combinations of PA-specific monoclonal antibodies for their ability to neutralize anthrax toxin in cell culture assays. We observed additive, synergistic, and antagonistic effects of the antibodies depending on the specific antibody combination examined and the specific assay used. Synergistic toxin-neutralizing antibody interactions were examined in more detail. We found that one mechanism that can lead to antibody synergy is the bridging of PA monomers by one antibody, with resultant bivalent binding of the second antibody. These results may aid in optimal design of new vaccines and antibody therapies against anthrax.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.046
GPT teacher head0.359
Teacher spread0.312 · 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

Citations18
Published2012
Admission routes1
Has abstractyes

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