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Record W2561114774 · doi:10.1038/srep38976

The role of GRIP1 and ephrin B3 in blood pressure control and vascular smooth muscle cell contractility

2016· article· en· W2561114774 on OpenAlexafffund
Yujia Wang, Zenghui Wu, Hongyu Luo, Junzheng Peng, John Raelson, Georg Ehret, Patricia B. Munroe, Ékatherina Stoyanova, Zhao Qin, Guy Cloutier, Wayne E. Bradley, Tao Wu, Jianzhong Shen, Shen‐Jiang Hu, Jiangping Wu

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchRegeneron Pharmaceuticals
KeywordsVascular smooth muscleBlood pressureGene knockdownContractilityReceptorBiologyInternal medicineEndocrinologyCell biologyMedicineGeneSmooth muscleBiochemistry

Abstract

fetched live from OpenAlex

Several erythropoietin-producing hepatocellular receptor B family (EPHB) and their ligands, ephrinBs (EFNBs), are involved in blood pressure regulation in animal models. We selected 528 single nucleotide polymorphisms (SNPs) within the genes of EPHB6, EFNB2, EFNB3 and GRIP1 in the EPH/EFN signalling system to query the International Blood Pressure Consortium dataset. A SNP within the glutamate receptor interacting protein 1 (GRIP1) gene presented a p-value of 0.000389, approaching the critical p-value of 0.000302, for association with diastolic blood pressure of 60,396 individuals. According to echocardiography, we found that Efnb3 gene knockout mice showed enhanced constriction in the carotid arteries. In vitro studies revealed that in mouse vascular smooth muscle cells, siRNA knockdown of GRIP1, which is in the EFNB3 reverse signalling pathway, resulted in increased contractility of these cells. These data suggest that molecules in the EPHB/EFNB signalling pathways, specifically EFNB3 and GRIP1, are involved blood pressure regulation.

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)0.001
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 score0.139
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.211
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 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

Citations15
Published2016
Admission routes2
Has abstractyes

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