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Record W3111384439 · doi:10.1002/adma.202070373

Vaccines: SARS‐CoV‐2 RBD Neutralizing Antibody Induction is Enhanced by Particulate Vaccination (Adv. Mater. 50/2020)

2020· article· en· W3111384439 on OpenAlexaff
Wei‐Chiao Huang, Shiqi Zhou, Xuedan He, Kevin Chiem, Moustafa T. Mabrouk, Ruth H. Nissly, Ian M. Bird, Mike Strauss, Suryaprakash Sambhara, Joaquı́n Ortega, Elizabeth A. Wohlfert, Luis Martínez‐Sobrido, Suresh V. Kuchipudi, Bruce A. Davidson, Jonathan F. Lovell

Bibliographic record

VenueAdvanced Materials · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsMcGill University
Fundersnot available
KeywordsImmunogenVirologyVaccinationNeutralizing antibodyAdjuvantImmunizationAntibodyCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Immune systemRecombinant DNAAntibody responseNeutralizationVirusBiologyImmunologyMedicineMonoclonal antibody

Abstract

fetched live from OpenAlex

In article number 2005637, Jonathan F. Lovell and co-workers show that the SARS-CoV-2 RBD surface protein becomes a potent immunogen when presented in nanoparticle format. Using a vaccine adjuvant that spontaneously converts soluble recombinant antigens into stable particles, immunization studies in mice and rabbits shows that the particle-based RBD elicits strong immune responses and potent antibodies capable of neutralizing the virus.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.290
Teacher spread0.275 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2020
Admission routes1
Has abstractyes

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