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Record W4387392387 · doi:10.1126/science.adj0070

Mapping SARS-CoV-2 antigenic relationships and serological responses

2023· article· en· W4387392387 on OpenAlexafffund
Samuel Wilks, Barbara Mühlemann, Xiaoying Shen, Sina Türeli, Eric B. LeGresley, Antonia Netzl, Miguela A. Caniza, Jesus N. Chacaltana-Huarcaya, Victor M. Corman, Xiaoju Daniell, Michael Datto, Fatimah S. Dawood, Thomas N. Denny, Christian Drosten, Ron A. M. Fouchier, Patricia García, Peter Halfmann, Agatha N. Jassem, Lara M. Jeworowski, Terry C. Jones, Yoshihiro Kawaoka, Florian Krammer, Charlene McDanal, Rolando Pajón, Viviana Simon, Melissa S. Stockwell, Haili Tang, Harm van Bakel, Vic Veguilla, Richard J. Webby, David C. Montefiori, Derek J. Smith

Bibliographic record

VenueScience · 2023
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsBC Centre for Disease Control
FundersDivision of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious DiseasesNational Institute of Allergy and Infectious DiseasesSeqirusNational Institutes of HealthShionogiDeutsches Zentrum für InfektionsforschungGates Cambridge TrustDaiichi Sankyo EuropeUniversity of British ColumbiaCambridge TrustBundesministerium für GesundheitFUJIFILM Toyama ChemicalBundesministerium für Bildung und ForschungPfizerIcahn School of Medicine at Mount SinaiBiomedical Advanced Research and Development AuthorityCenters for Disease Control and PreventionBritish Columbia Centre for Disease ControlOtsuka PharmaceuticalJapan Agency for Medical Research and DevelopmentNational Center for Immunization and Respiratory DiseasesProvincial Health Services AuthorityModerna
KeywordsSerologyVirologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)Antigen2019-20 coronavirus outbreakBiologyImmunologyAntibodyMedicineInfectious disease (medical specialty)DiseaseOutbreak

Abstract

fetched live from OpenAlex

During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, multiple variants escaping preexisting immunity emerged, causing reinfections of previously exposed individuals. Here, we used antigenic cartography to analyze patterns of cross-reactivity among 21 variants and 15 groups of human sera obtained after primary infection with 10 different variants or after messenger RNA (mRNA)-1273 or mRNA-1273.351 vaccination. We found antigenic differences among pre-Omicron variants caused by substitutions at spike-protein positions 417, 452, 484, and 501. Quantifying changes in response breadth over time and with additional vaccine doses, our results show the largest increase between 4 weeks and >3 months after a second dose. We found changes in immunodominance of different spike regions, depending on the variant an individual was first exposed to, with implications for variant risk assessment and vaccine-strain selection.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.585
Threshold uncertainty score0.625

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
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 head0.207
GPT teacher head0.415
Teacher spread0.208 · 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

Citations135
Published2023
Admission routes2
Has abstractyes

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