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Record W2033837731 · doi:10.1002/cjce.20290

Mechanotransduction and blood fluid dynamics in developing blood vessels

2010· article· en· W2033837731 on OpenAlexaffvenue
Elizabeth A. V. Jones

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAngiogenesis and VEGF in Cancer
Canadian institutionsMcGill University
Fundersnot available
KeywordsMechanotransductionEmbryonic stem cellBlood flowBlood vesselCell biologyBiologyArteriogenesisEndotheliumAngiogenesisHomeostasisShear stressAnatomyPathologyNeuroscienceMedicineInternal medicineCancer researchEndocrinologyGeneGeneticsMechanics

Abstract

fetched live from OpenAlex

Abstract The vascular endothelium is the interface in the cardiovascular system between the blood vessel wall and the flowing blood. As such, these cells are exposed to both shear stress and circumferential stretch. Though a lot is known about the regulation of gene expression by flow in mature vascular networks, very little is known in developing vessels. Most vascular networks in the adult are homeostatic, exhibiting very low rates of endothelial cell replication and turnover. In disease states such as cancer or macular degeneration, the vascular system is able to recapitulate embryonic growth and reinduce blood vessel growth. The vasculature that develops is similar to the embryonic vasculature, and so many have used knowledge of embryonic development to interpret pathological blood vessel growth. It was believed until recently that the embryonic vasculature was not sensitive to flow. We recently showed that shear stress is necessary for proper vascular development. We therefore review the role of blood flow and mechanical forces in vascular development. We examine the pattern and magnitude of flow present in primitive vascular networks as well as exploring gene regulation by shear stress in both in vitro and in vivo embryonic systems.

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 score0.002
Threshold uncertainty score0.241

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 head0.005
GPT teacher head0.190
Teacher spread0.185 · 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

Citations6
Published2010
Admission routes2
Has abstractyes

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