MétaCan
Menu
Back to cohort
Record W2117451256 · doi:10.1083/jcb.201007056

Dynamic macrophage “probing” is required for the efficient capture of phagocytic targets

2010· article· en· W2117451256 on OpenAlexafffund
Ronald S. Flannagan, Rene E. Harrison, Christopher M. Yip, Khuloud Jaqaman, Sergio Grinstein

Bibliographic record

VenueThe Journal of Cell Biology · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of TorontoThe Scarborough HospitalHospital for Sick Children
FundersNational Institute of General Medical SciencesCanadian Institutes of Health Research
KeywordsOpsoninBiologyCell biologyMacrophageReceptorActinPhosphatidylinositolPhagocytosisBiophysicsSignal transductionBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Binding of ligands by immunoreceptors is thought to be a passive, stochastic process. Contrary to this notion, we found that binding of IgG-opsonized particles by Fcγ receptors was inhibited in macrophages, dendritic and microglial cells by agents that interfere with actin assembly or disassembly. Changes in the lateral mobility of the receptors--assessed by single-particle tracking--or in the microelasticity of the membrane--determined by atomic-force microscopy--could not account for the effects of actin disruption on particle binding. Instead, we found that the macrophages contact their targets by actively extending actin-rich structures. Formation of these protrusions is driven by Rac and requires phosphatidylinositol 4,5-bisphosphate and phosphatidylinositol 3,4,5-trisphosphate. Capture of C3bi-opsonized as well as unopsonized targets by macrophages was also dependent on actin. Thus, phagocytes continuously probe their environment for foreign particles in a manner akin to the constitutive sampling of the fluid milieu by dendritic cells. Active probing by phagocytes is most important when confronted by scarcely opsonized and/or highly mobile targets.

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)0.001
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 score0.056
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.245
Teacher spread0.238 · 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

Citations138
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Cell BiologySame topicImmunotherapy and Immune ResponsesFrench-language works237,207