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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.066
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