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Record W1997892103 · doi:10.1089/vim.2006.0064

Inhibition of Severe Acute Respiratory Syndrome-Associated Coronavirus Infection by Equine Neutralizing Antibody in Golden Syrian Hamsters

2007· article· en· W1997892103 on OpenAlexaff
Guangyu Zhao, Bing Ni, Haiyan Jiang, Deyan Luo, Marek Pacal, Lili Zhou, Liangyan Zhang, Li Xing, Zhengcai Jia, Zhihua Lin, Li Wang, Jintao Li, Yunfei Liang, Xinfu Shi, Tingting Zhao, Liyun Zhou, Yuzhang Wu, Xiliang Wang

Bibliographic record

VenueViral Immunology · 2007
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsAntibodyVirologyCoronavirusImmunologyNeutralizing antibodyRespiratory systemMedicineMesocricetusHamsterTiterSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Antibody titerMiddle East respiratory syndrome coronavirusCoronavirus disease 2019 (COVID-19)DiseaseInfectious disease (medical specialty)Internal medicine

Abstract

fetched live from OpenAlex

Equine anti-severe acute respiratory syndrome-associated coronavirus F(ab')(2) has been verified to protect mice from infection with severe acute respiratory syndrome-associated coronavirus (SARS-CoV). However, before potential clinical application, the antibody needs to be tested in as many animal models as possible to ensure its safety and efficiency. In this study, after verification by various methods that the golden Syrian hamster constitutes a model susceptible to SARS-CoV infection, we confirmed that the antibody could protect animals completely from SARS-CoV infection in the preventive setting. More importantly, the antibody could reduce viral titers or copies by approximately 10(3)- to 10(4)-fold in animal lung after virus exposure, compared with negative control. These data provide further evidence to warrant clinical studies of this antibody in the treatment and prevention of SARS.

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 score0.331
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.361
Teacher spread0.327 · 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

Citations18
Published2007
Admission routes1
Has abstractyes

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