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Record W2784649851

Abstract 14239: Wall Stress-Mediated Rgs5 Expression in Vascular Smooth Muscle Cells Controls Arterial Remodeling During Hypertension

2016· article· en· W2784649851 on OpenAlexaff
Caroline Arnold, Eda Demirel, Guillem Genové, Hangjun Zhang, Thomas Wieland, Markus Hecker, Scott P. Heximer, Thomas Korff

Bibliographic record

VenueCirculation · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVascular smooth muscleMedicineArteriogenesisRHOATranscriptomeInternal medicineContraction (grammar)Cell biologyEndocrinologySignal transductionAnatomyGene expressionBiologySmooth muscleAngiogenesisGene
DOInot available

Abstract

fetched live from OpenAlex

The onset of hypertension is characterized by an increase in arterial wall stress which results in a compensatory remodeling of the vascular wall which ultimately leads to arterial stiffening. While G-protein signaling has been reported to control contraction and differentiation of vascular smooth muscle cells (VSMCs) in this context, the role of the regulators of G-protein signaling (RGS) has not been elucidated in detail. While earlier reports demonstrate that RGS5 regulates biomechanically induced arterial growth (arteriogenesis), we aimed at investigating the relevance of RGS5 for hypertension-induced VSMC responses. In vitro, exposing VSMCs to biomechanical stretch increased RGS5 protein abundance (3.2-fold, p<0.05, n=3). Likewise, RGS5 mRNA expression was augmented in isolated arteries in response to increased wall stress (1.7-fold, p<0.05, n=13). As evidenced by a whole genome microarray, overexpression of RGS5 under these conditions shifted the transcriptome towards expression of RhoA pathway-asso...

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.011
GPT teacher head0.212
Teacher spread0.202 · 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 source (direct Gemma or distilled Codex), 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
Published2016
Admission routes1
Has abstractyes

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