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Record W2771167151 · doi:10.1080/21541248.2017.1332505

Go with the flow: GEF-H1 mediated shear stress mechanotransduction in neutrophils

2017· review· en· W2771167151 on OpenAlexaff
Noah Fine, Ioannis D. Dimitriou, Robert Rottapel

Bibliographic record

VenueSmall GTPases · 2017
Typereview
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMechanotransductionCell biologyMyosin light-chain kinaseContractilityRHOABiologyMyosinGuanine nucleotide exchange factorStress fiberShear stressBiophysicsPhosphorylationSignal transductionFocal adhesionMaterials science

Abstract

fetched live from OpenAlex

Neutrophils in circulation experience significant shear forces due to blood flow when they tether to the vascular endothelium. Biochemical and biophysical responses of neutrophils to the physical force of flowing blood modulate their behavior and promote tissue recruitment under pro-inflammatory conditions. Neutrophil mechanotransduction responses occur through mechanisms that are not yet fully understood. In our recent work, we showed that GEF-H1, a RhoA specific guanine nucleotide exchange factor (GEF), is required to maintain neutrophil motility and migration in response to shear stress. GEF-H1 re-localizes to flottilin-rich uropods in neutrophils in response to fluid shear stress and promotes spreading and crawling on activated endothelial cells. GEF-H1 drives cellular contractility through myosin light chain (MLC) phosphorylation downstream of the Rho-ROCK signaling axis. We propose that GEF-H1-dependent cell spreading and crawling in shear stress-dependent neutrophil recruitment from the vasculature are due to the specific localization of Rho-induced contractility in the uropod.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.120
GPT teacher head0.366
Teacher spread0.246 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2017
Admission routes1
Has abstractyes

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