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Record W2045673606 · doi:10.1371/journal.pntd.0001575

Ebola GP-Specific Monoclonal Antibodies Protect Mice and Guinea Pigs from Lethal Ebola Virus Infection

2012· article· en· W2045673606 on OpenAlexafffund
Xiangguo Qiu, Lisa Fernando, Pasquale L. Melito, Jonathan Audet, Heinz Feldmann, Gary Wong, Judie B. Alimonti

Bibliographic record

VenuePLoS neglected tropical diseases · 2012
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsEbola virusMonoclonal antibodyVirologyLethal doseGuinea pigEbolavirusAntibodyBiologyVirusEbola Hemorrhagic FeverMedicineImmunology

Abstract

fetched live from OpenAlex

Ebola virus (EBOV) causes acute hemorrhagic fever in humans and non-human primates with mortality rates up to 90%. So far there are no effective treatments available. This study evaluates the protective efficacy of 8 monoclonal antibodies (MAbs) against Ebola glycoprotein in mice and guinea pigs. Immunocompetent mice or guinea pigs were given MAbs i.p. in various doses individually or as pools of 3-4 MAbs to test their protection against a lethal challenge with mouse- or guinea pig-adapted EBOV. Each of the 8 MAbs (100 µg) protected mice from a lethal EBOV challenge when administered 1 day before or after challenge. Seven MAbs were effective 2 days post-infection (dpi), with 1 MAb demonstrating partial protection 3 dpi. In the guinea pigs each MAb showed partial protection at 1 dpi, however the mean time to death was significantly prolonged compared to the control group. Moreover, treatment with pools of 3-4 MAbs completely protected the majority of animals, while administration at 2-3 dpi achieved 50-100% protection. This data suggests that the MAbs generated are capable of protecting both animal species against lethal Ebola virus challenge. These results indicate that MAbs particularly when used as an oligoclonal set are a potential therapeutic for post-exposure treatment of EBOV 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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.035
GPT teacher head0.292
Teacher spread0.257 · 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 designObservational
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

Citations99
Published2012
Admission routes2
Has abstractyes

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