MétaCan
Menu
Back to cohort
Record W4206365198 · doi:10.3390/v14010144

Antigenicity of the Mu (B.1.621) and A.2.5 SARS-CoV-2 Spikes

2022· article· en· W4206365198 on OpenAlexaff
Debashree Chatterjee, Alexandra Tauzin, Annemarie Laumaea, Shang Yu Gong, Yuxia Bo, Aurélie Guilbault, Guillaume Goyette, Catherine Bourassa, Gabrielle Gendron‐Lepage, Halima Medjahed, Jonathan Richard, Sandrine Moreira, Marceline Côté, Andrés Finzi

Bibliographic record

VenueViruses · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsInstitut National de Santé Publique du QuébecUniversity of OttawaUniversité de MontréalMcGill UniversityCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsAntigenicitySpike (software development)ReceptorMutationNeutralizationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)VirologyCoronavirus disease 2019 (COVID-19)BiologySpike ProteinMolecular biologyVirusAntibodyGeneticsGeneMedicineInternal medicine

Abstract

fetched live from OpenAlex

The rapid emergence of SARS-CoV-2 variants is fueling the recent waves of the COVID-19 pandemic. Here, we assessed ACE2 binding and antigenicity of Mu (B.1.621) and A.2.5 Spikes. Both these variants carry some mutations shared by other emerging variants. Some of the pivotal mutations such as N501Y and E484K in the receptor-binding domain (RBD) detected in B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) are now present within the Mu variant. Similarly, the L452R mutation of B.1.617.2 (Delta) variant is present in A.2.5. In this study, we observed that these Spike variants bound better to the ACE2 receptor in a temperature-dependent manner. Pseudoviral particles bearing the Spike of Mu were similarly neutralized by plasma from vaccinated individuals than those carrying the Beta (B.1.351) and Delta (B.1.617.2) Spikes. Altogether, our results indicate the importance of measuring critical parameters such as ACE2 interaction, plasma recognition and neutralization ability of each emerging variant.

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 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 score0.263
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.083
GPT teacher head0.367
Teacher spread0.284 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueVirusesSame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207