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Record W4366161256 · doi:10.1016/j.xpro.2023.102224

A co-culture model system to quantify antibody-dependent cellular cytotoxicity in human breast cancer cells using an engineered natural killer cell line

2023· article· en· W4366161256 on OpenAlexfundno aff
Roos Vincken, Ana Ruiz-Sáenz

Bibliographic record

VenueSTAR Protocols · 2023
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
FundersHORIZON EUROPE Marie Sklodowska-Curie ActionsH2020 Marie Skłodowska-Curie ActionsHorizon 2020Medical Research CouncilHorizon 2020 Framework ProgrammeMinistero della SaluteNatural Sciences and Engineering Research Council of CanadaEuropean CommissionWeston Brain InstituteCanadian Institutes of Health ResearchAlzheimer SocietyNational Institute for Health and Care ResearchAlzheimer's Society
KeywordsAntibody-dependent cell-mediated cytotoxicityCytotoxicityCD16Peripheral blood mononuclear cellImmune systemCell cultureImmunologyJurkat cellsCancerAntibodyCancer researchNatural killer cellFlow cytometryBiologyCancer cellIn vitroT cellCD3Biochemistry

Abstract

fetched live from OpenAlex

Current protocols measure antibody-dependent cellular cytotoxicity (ADCC) in vitro using peripheral blood mononuclear cells (PBMCs), but isolation and variability among donors limit the viability and reproducibility of this approach. Here, we present a standardized co-culture model system to quantify ADCC on human breast cancer cells. We describe steps to engineer a natural killer cell line that stably expresses FCγRIIIa (CD16), required to mediate ADCC. We then detail the steps for the cancer-immune co-culture setup, followed by cytotoxicity measurement and analysis.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.063
GPT teacher head0.412
Teacher spread0.350 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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