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Record W2009286693 · doi:10.1089/vim.2008.0064

SARS-CoV Spike Proteins Expressed by the Vaccinia Virus Tiantan Strain: Secreted SQ Protein Induces Robust Neutralization Antibody in Mice

2009· article· en· W2009286693 on OpenAlexaff
Kexia Yan, Wenjie Tan, Huijuan Wang, Yue Wang, Xiangmin Zhang, Yan Li, Li Ruan

Bibliographic record

VenueViral Immunology · 2009
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsCanadian Science Centre for Human and Animal Health
FundersNational High-tech Research and Development Program
KeywordsVirologyImmunogenicityAntibodyNeutralizationGlycoproteinAntigenicityVacciniaVirusBiologyCoronavirusAntigenNeutralizing antibodySpike ProteinFusion proteinRecombinant DNAGeneMedicineImmunologyMolecular biologyCoronavirus disease 2019 (COVID-19)Genetics

Abstract

fetched live from OpenAlex

The spike (S) glycoprotein of severe acute respiratory syndrome coronavirus (SARS-CoV) is a major target in the development of diagnostic assays and vaccines, but its antigenic and immunogenic properties remain unclear. Seven SARS-CoV spike proteins (S, SQ, S1, RBD, S2, S2Q, and CX) were generated using the modified vaccinia virus (Tiantan strain) as a vector, and their antigenicity and immunogenicity were evaluated. The secreted SQ protein in which the transmembrane domain was deleted, as well as the full-length spike protein, showed the most potential to induce the production of neutralizing antibody (nAb) in mice. S1 and RBD proteins initialized significantly lower levels of nAb production. In addition, the S proteins were recognized specifically by the sera of convalescent patients with SARS, and that of mice immunized with inactivated SARS-CoV, but did not react with anti-sera of HCoV-OC43 or HCoV-229E, or sera from healthy donors (although RBD showed a false-positive in 1 of 55 control samples of human sera). Our results demonstrate that SQ protein may be an effective vaccine candidate and a convenient and safe diagnostic antigen for SARS-CoV.

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 categoriesMeta-epidemiology (narrow)
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 score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.035
GPT teacher head0.332
Teacher spread0.297 · 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.

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

Citations10
Published2009
Admission routes1
Has abstractyes

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