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Record W1921028801 · doi:10.1093/infdis/jiv449

Escape From Monoclonal Antibody Neutralization Affects Henipavirus Fitness In Vitro and In Vivo

2015· article· en· W1921028801 on OpenAlexaff
Viktoriya Borisevich, Benhur Lee, Andrew C. Hickey, Blair L. DeBuysscher, Christopher C. Broder, Heinz Feldmann, Barry Rockx

Bibliographic record

VenueThe Journal of Infectious Diseases · 2015
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of Health
KeywordsNeutralizationVirologyMonoclonal antibodyBiologyAntibodyIn vitroNeutralizing antibodyIn vivoVirulenceViral replicationVirusGlycoproteinImmunologyGeneMolecular biologyGenetics

Abstract

fetched live from OpenAlex

Henipaviruses are zoonotic viruses that can cause severe and acute respiratory diseases and encephalitis in humans. To date, no vaccine or treatments are approved for human use. The presence of neutralizing antibodies is a strong correlate of protection against lethal disease in animals. However, since RNA viruses are prone to high mutation rates, the possibility that these viruses will escape neutralization remains a potential concern. In the present study, we generated neutralization-escape mutants, using 6 different monoclonal antibodies, and studied the effect of these neutralization-escape mutations on in vitro and in vivo fitness. These data provide a mechanism for overcoming neutralization escape by use of cocktails of cross-neutralizing monoclonal antibodies that recognize residues within the glycoprotein that are important for virus replication and virulence.

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 score0.002

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 integrity0.0000.001
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 head0.016
GPT teacher head0.295
Teacher spread0.279 · 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

Citations30
Published2015
Admission routes1
Has abstractyes

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