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Force-Induced Polarized Mitosis of Endothelial and Smooth Muscle Cells in Arterial Remodeling

2007· article· en· W2116775810 on OpenAlexafffund
Dorota Dajnowiec, Peter Sabatini, Thea Van Rossum, Jacky T.K. Lam, Ming Zhang, András Kapùs, B. Lowell Langille

Bibliographic record

VenueHypertension · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversity of TorontoUniversity Health NetworkSt. Michael's Hospital
FundersHeart and Stroke Foundation of Canada
KeywordsMitosisCell biologyCortactinAnatomyVascular smooth muscleBiologyChemistryBiophysicsCellSmooth muscleCytoskeletonEndocrinology

Abstract

fetched live from OpenAlex

Arteries display highly directional growth and remodeling that are specific to increases in the mechanical loads imposed on them by blood pressure, blood flow, and lengthwise tensile forces that are transmitted from the tissues to which they are attached. This study examined the effect of mechanical forces on the direction in which mitosis delivers daughter cells, as a mechanism for directional growth. Lateral forces were imposed on surface integrins of cultured endothelial cells by seeding the cells with arginine-glycine-aspartate peptide-coated magnetic microspheres and applying a magnetic field. Video images revealed that the mitotic axis of dividing cells became highly biased in the direction of applied force. Distribution of cortactin, which participates in polarized mitoses driven by other stimuli, was highly sensitive to mechanical loading and interfering with cortactin function arrested cell growth. Smooth muscle cell mitoses also proved to be sensitive to mechanical force: when lengthwise force imposed on rabbit carotid arteries was altered by excision of a vessel segment and reanastomosis of the cut ends, direction of mitosis was dramatically altered. These findings indicate that influences of mechanical force can modulate the manner in which mitosis of vascular cells contributes to reorganization of arterial wall tissue.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.325

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 head0.233
Teacher spread0.216 · 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

Citations14
Published2007
Admission routes2
Has abstractyes

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