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Record W2401099396 · doi:10.1007/978-1-61779-139-0_9

Ratiometric Analysis of Subcellular Recruitment of Fc Receptors During Phagocytosis

2011· article· en· W2401099396 on OpenAlexaff
Patricia Mero, James W. Booth

Bibliographic record

VenueMethods in molecular biology · 2011
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsSunnybrook Health Science CentreSunnybrook HospitalUniversity of Toronto
Fundersnot available
KeywordsPhagocytosisReceptorCell biologyContext (archaeology)Subcellular localizationCell surface receptorBiologySignal transductionActin cytoskeletonImmune systemCytoskeletonCellChemistryBiochemistryImmunologyCytoplasm

Abstract

fetched live from OpenAlex

Numerous immune receptors have the ability to mediate phagocytosis of large particles by triggering dynamic local rearrangement of the cytoskeleton and cell membrane. Different receptors can be differentially recruited to sites of particle binding, which in turn can have important functional consequences with respect to engulfment and downstream signaling. Using Fcγ receptor-mediated phagocytosis of IgG-coated particles as a model, we describe a method for analyzing nascent phagocytic cups and quantifying relative receptor levels at sites of phagocytosis. This technique is based on a ratiometric analysis of subcellular localization of exogenously expressed receptors carrying different fluorescent protein tags. This approach could be applied more generally to the analysis of surface membrane protein localization in the context of any dynamic cellular process.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.168
GPT teacher head0.475
Teacher spread0.306 · 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
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

Citations2
Published2011
Admission routes1
Has abstractyes

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