MétaCan
Menu
Back to cohort
Record W3201465512 · doi:10.1016/j.xpro.2021.100851

A new flow cytometry assay to measure antibody-dependent cellular cytotoxicity against SARS-CoV-2 Spike-expressing cells

2021· article· en· W3201465512 on OpenAlexafffund
Guillaume Beaudoin-Bussières, Jonathan Richard, Jérémie Prévost, Guillaume Goyette, Andrés Finzi

Bibliographic record

VenueSTAR Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsUniversité de MontréalMcGill UniversityCentre Hospitalier de l’Université de Montréal
FundersNational Institute of Allergy and Infectious DiseasesFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchNational Institutes of HealthCanada Foundation for InnovationFondation du CHUM
KeywordsAntibody-dependent cell-mediated cytotoxicityFlow cytometryMonoclonal antibodyAntibodyCytotoxicityEffectorVirologyJurkat cellsBiologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)ImmunologyCoronavirus disease 2019 (COVID-19)Immune systemMedicineIn vitroT cellGenetics

Abstract

fetched live from OpenAlex

Antibodies can engage specific receptors at the surface of effector cells and mediate several functions beyond viral neutralization. Increasing evidence suggests that Fc-mediated effector functions, such as antibody-dependent cellular cytotoxicity (ADCC), have an important role in protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We engineered a cell line stably expressing a GFP-tagged SARS-CoV-2 spike to measure ADCC. This protocol provides an optimized way of measuring ADCC activity mediated by anti-SARS-CoV-2 Spike monoclonal antibodies or plasma from previously infected or vaccinated individuals. For complete details on the use and execution of this protocol, please refer to Anand et al. (2021b).

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0120.006

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 head0.066
GPT teacher head0.386
Teacher spread0.320 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations43
Published2021
Admission routes2
Has abstractyes

Explore more

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