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Record W2912562972 · doi:10.3390/v11020092

Human Polyclonal Antibodies Produced from Transchromosomal Bovine Provides Prophylactic and Therapeutic Protections Against Zika Virus Infection in STAT2 KO Syrian Hamsters

2019· article· en· W2912562972 on OpenAlexaff
Venkatraman Siddharthan, Jinxin Miao, Arnaud J. Van Wettere, Rong Li, Hua Wu, Eddie J. Sullivan, Jin‐an Jiao, Jay W. Hooper, David Safronetz, John D. Morrey, Justin G. Julander, Zhongde Wang

Bibliographic record

VenueViruses · 2019
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsPublic Health Agency of Canada
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthUtah Agricultural Experiment Station
KeywordsZika virusPolyclonal antibodiesVirologyAntibodyImmunologyMedicineMicrocephalyVirusBiology

Abstract

fetched live from OpenAlex

Zika virus (ZIKV) infection can cause severe congenital diseases, such as microcephaly, ocular defects and arthrogryposis in fetuses, and Guillain–Barré syndrome in adults. Efficacious therapeutic treatments for infected patients, as well as prophylactic treatments to prevent new infections are needed for combating ZIKV infection. Here, we report that ZIKV-specific human polyclonal antibodies (SAB-155), elicited in transchromosomal bovine (TcB), provide significant protection from infection by ZIKV in STAT2 knockout (KO) golden Syrian hamsters both prophylactically and therapeutically. These antibodies also prevent testicular lesions in this hamster model. Our data indicate that antibody-mediated immunotherapy is effective in treating ZIKV infection. Because suitable quantities of highly potent human polyclonal antibodies can be quickly produced from the TcB system against ZIKV and have demonstrated therapeutic efficacy in a small animal model, they have the potential as an effective countermeasure against ZIKV 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 score0.618
Threshold uncertainty score0.918

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)0.0000.000

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 head0.018
GPT teacher head0.278
Teacher spread0.260 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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