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Record W2895927433 · doi:10.1002/ar.23962

Regulation and Assembly of Actomyosin Contractile Rings in Cytokinesis and Cell Repair

2018· review· en· W2895927433 on OpenAlexafffund
Corina DeKraker, Éric Boucher, Craig A. Mandato

Bibliographic record

VenueThe Anatomical Record · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCytokinesisCell biologyChemistryBiophysicsCellBiologyCell divisionBiochemistry

Abstract

fetched live from OpenAlex

ABSTRACT Cytokinesis and single‐cell wound repair both involve contractile assemblies of filamentous actin (F‐actin) and myosin II organized into characteristic ring‐like arrays. The assembly of these actomyosin contractile rings (CRs) is specified spatially and temporally by small Rho GTPases, which trigger local actin polymerization and myosin II contractility via a variety of downstream effectors. We now have a much clearer view of the Rho GTPase signaling cascade that leads to the formation of CRs, but some factors involved in CR positioning, assembly, and function remain poorly understood. Recent studies show that this regulation is multifactorial and goes beyond the long‐established Ca 2+ ‐dependent processes. There is substantial evidence that the Ca 2+ ‐independent changes in cell shape, tension, and plasma membrane composition that characterize cytokinesis and single‐cell wound repair also regulate CR formation. Elucidating the regulation and mechanistic properties of CRs is important to our understanding of basic cell biology and holds potential for therapeutic applications in human disease. In this review, we present a primer on the factors influencing and regulating CR positioning, assembly, and contraction as they occur in a variety of cytokinetic and single‐cell wound repair models. Anat Rec, 301:2051–2066, 2018. © 2018 Wiley Periodicals, Inc.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.990
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.287
Teacher spread0.270 · 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 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
Published2018
Admission routes2
Has abstractyes

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