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Record W2406515641 · doi:10.1038/srep25856

Potent neutralizing monoclonal antibodies against Ebola virus infection

2016· article· en· W2406515641 on OpenAlexafffund
Qi Zhang, Miao Gui, Xuefeng Niu, Shihua He, Ruoke Wang, Yupeng Feng, Andrea Kroeker, Yanan Zuo, Hua Wang, Ying Wang, Jiade Li, Chufang Li, Yi Shi, Xuanling Shi, George F. Gao, Ye Xiang, Xiangguo Qiu, Ling Chen, Linqi Zhang

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersInstitute of Microbiology, Chinese Academy of SciencesTsinghua UniversityChinese Academy of SciencesPublic Health AgencyPublic Health Agency of CanadaNational Science Foundation
KeywordsEbola virusVirologyMonoclonal antibodyVirusEpitopeEbolavirusAntibodyNPC1Neutralizing antibodyBiologyImmunizationImmunologyCell

Abstract

fetched live from OpenAlex

Ebola virus infections cause a deadly hemorrhagic disease for which no vaccines or therapeutics has received regulatory approval. Here we show isolation of three (Q206, Q314 and Q411) neutralizing monoclonal antibodies (mAbs) against the surface glycoprotein (GP) of Ebola virus identified in West Africa in 2014 through sequential immunization of Chinese rhesus macaques and antigen-specific single B cell sorting. These mAbs demonstrated potent neutralizing activities against both pseudo and live Ebola virus independent of complement. Biochemical, single particle EM, and mutagenesis analysis suggested Q206 and Q411 recognized novel epitopes in the head while Q314 targeted the glycan cap in the GP1 subunit. Q206 and Q411 appeared to influence GP binding to its receptor NPC1. Treatment with these mAbs provided partial but significant protection against disease in a mouse model of Ebola virus infection. These novel mAbs could serve as promising candidates for prophylactic and therapeutic interventions against Ebola virus infection.

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 score0.169
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.035
GPT teacher head0.323
Teacher spread0.288 · 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

Citations50
Published2016
Admission routes2
Has abstractyes

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