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Record W2293882857 · doi:10.1126/scitranslmed.aad9875

Two-mAb cocktail protects macaques against the Makona variant of Ebola virus

2016· article· en· W2293882857 on OpenAlexafffund
Xiangguo Qiu, Jonathan Audet, Ming Lv, Shihua He, Haiyan Wei, Longlong Luo, Lisa Fernando, Andrea Kroeker, Hugues Fausther Bovendo, Alexander Bello, Feng Li, Pei Ye, Michael Jacobs, Giuseppe Ippolito, Erica Ollmann Saphire, Shengli Bi, Beifen Shen, George F. Gao, Larry Zeitlin, Jiannan Feng, Boyan Zhang, Gary Wong

Bibliographic record

VenueScience Translational Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchNational Institutes of HealthNational Natural Science Foundation of China
KeywordsEbola virusMonoclonal antibodyVirologyVirusBiologyEbolavirusAntibodyImmunology

Abstract

fetched live from OpenAlex

The 2014-2015 Ebola virus (EBOV) outbreak in West Africa highlighted the urgent need for specific therapeutic interventions for infected patients. The human-mouse chimeric monoclonal antibody (mAb) cocktail ZMapp, previously shown to be efficacious in EBOV (variant Kikwit) lethally infected nonhuman primates (NHPs) when administration was initiated up to 5 days, was used in some patients during the outbreak. We show that a two-antibody cocktail, MIL77E, is fully protective in NHPs when administered at 50 mg/kg 3 days after challenge with a lethal dose of EBOV variant Makona, the virus responsible for the ongoing 2014-2015 outbreak, whereas a similar formulation of ZMapp protected two of three NHPs. The chimeric MIL77E mAb cocktail is produced in engineered Chinese hamster ovary cells and is based on mAbs c13C6 and c2G4 from ZMapp. The use of only two antibodies in MIL77E opens the door to a pan-ebolavirus cocktail.

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)0.001
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 score0.251
Threshold uncertainty score0.870

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.039
GPT teacher head0.358
Teacher spread0.318 · 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

Citations88
Published2016
Admission routes2
Has abstractyes

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