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Record W4289856951 · doi:10.1101/2022.08.03.22278386

SARS-CoV-2 Omicron subvariants Spike recognition and neutralization elicited after the third dose of mRNA vaccine

2022· preprint· en· W4289856951 on OpenAlexafffund
Alexandra Tauzin, Alexandre Nicolas, Shilei Ding, Mehdi Benlarbi, Halima Medjahed, Debashree Chatterjee, Katrina Dionne, Shang Yu Gong, Gabrielle Gendron‐Lepage, Yuxia Bo, Josée Perreault, Guillaume Goyette, Laurie Gokool, Pascale Arlotto, Chantal Morrisseau, Cécile Tremblay, Valérie Martel‐Laferrière, Gaston De Serres, Inès Levade, Daniel E. Kaufmann, Marceline Côté, Renée Bazin, Andrés Finzi

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversity of OttawaInstitut National de Santé Publique du QuébecHéma-QuébecUniversité de MontréalMcGill UniversityCentre Hospitalier de l’Université de Montréal
FundersFondation du CHUM
KeywordsNeutralizationVirologyVaccinationHumoral immunitySpike (software development)AntibodyImmunologyMessenger RNABiologyImmunityImmune systemMedicineGeneGenetics

Abstract

fetched live from OpenAlex

SUMMARY Several SARS-CoV-2 Omicron subvariants have recently emerged, becoming the dominant circulating strains in many countries. These variants contain a large number of mutations in their Spike glycoprotein, raising concerns about vaccine efficacy. In this study, we evaluate the ability of plasma from a cohort of individuals that received three doses of mRNA vaccine to recognize and neutralize these Omicron subvariant Spikes. We observed that BA.4/5 and BQ.1.1 Spikes are markedly less recognized and neutralized compared to the D614G and the other Omicron subvariant Spikes tested. Also, individuals who have been infected before or after vaccination present better humoral responses than SARS-CoV-2 naïve vaccinated individuals, thus indicating that hybrid immunity generates better humoral responses against these subvariants.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.065
GPT teacher head0.341
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2022
Admission routes2
Has abstractyes

Explore more

Same venuemedRxiv→Same topicSARS-CoV-2 and COVID-19 Research→French-language works237,207→