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Record W2018310383 · doi:10.1086/599794

Development of Resistance to Passive Therapy with a Potently Neutralizing Humanized Monoclonal Antibody against West Nile Virus

2009· article· en· W2018310383 on OpenAlexaff
Shuliu Zhang, Matthew R. Vogt, Theodore Oliphant, Michael J. Engle, Evgeniy I. Bovshik, Michael Diamond, David W. C. Beasley

Bibliographic record

VenueThe Journal of Infectious Diseases · 2009
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of Allergy and Infectious Diseases
KeywordsVirologyNeutralizationMonoclonal antibodyHumanized antibodyVirusBiologyEpitopeAntibodyImmunotherapyNeutralizing antibodyWest Nile virusMonoclonalImmunologyImmune system

Abstract

fetched live from OpenAlex

Previous studies have established the therapeutic efficacy of humanized E16 (hE16) monoclonal antibody against West Nile virus in animals. Here, we assess the potential for West Nile virus strains encoding mutations in the hE16 epitope to resist passive immunotherapy and for the selection of neutralization escape variants during hE16 treatment. Resistance to hE16 in vivo was less common than expected, because several mutations that affected neutralization in vitro did not significantly affect protection in mice. Moreover, the emergence of resistant variants after infection with fully sensitive virus occurred but was relatively rare, even in highly immunocompromised B and T cell-deficient RAG mice.

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)0.001
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
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 head0.010
GPT teacher head0.274
Teacher spread0.265 · 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

Citations43
Published2009
Admission routes1
Has abstractyes

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