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cAMP‐signaling regulates the expression of atheroprotective genes in vascular endothelial cells exposed to differential fluid shear stresses (851.14)

2014· article· en· W1529819012 on OpenAlexafffundabout
Sarah Rampersad, Fabien Hubert, Paulina Brzezińska, Silja I. Freitag, M. Bibiana Umana, Alie Wudwud, Donald H. Maurice

Bibliographic record

VenueThe FASEB Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicApelin-related biomedical research
Canadian institutionsQueen's University
FundersCanadian Institutes of Health Research
KeywordsShear stressCell biologySignal transductionEffectorShear (geology)GeneBiologyPhysicsGeneticsMechanics

Abstract

fetched live from OpenAlex

Fluid‐mediated shear stress is an important, but often overlooked, mediator of vascular endothelial cell (VEC) health. VECs in the linear portions of macrovascular structures experience high laminar shear stress and exhibit an anti‐inflammatory and anti‐thrombotic phenotype. However, at vascular bifurcations and large curvatures fluid shear stress dramatically declines. Exposure to this low shear stress reduces VEC expression of vasculoprotective genes, making these areas preferential targets of atherosclerosis development. cAMP‐signaling is known to regulate a diverse array of VEC functions. Using various methodologies, we investigated the role of cAMP, and its downstream effectors, protein kinase A (PKA) and exchange protein activated by cAMP (EPAC)‐1, in regulating VEC health under differential fluid shear stresses. We report a novel role for cAMP, where modulation of this signaling pathway alters the expression of both atheroprotective and inflammatory genes in high and low shear stress conditions. This work has very important therapeutic implications for diseases characterized by endothelial cell dysfunction and atherosclerosis. Grant Funding Source : Canadian Institutes of Health Research

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)0.002
metaresearch head score (Gemma)0.001
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 score0.007
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.268
Teacher spread0.249 · 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

Citations0
Published2014
Admission routes3
Has abstractyes

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