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Record W2594553466 · doi:10.1038/srep44114

Evidence from single nucleotide polymorphism analyses of ADVANCE study demonstrates EFNB3 as a hypertension risk gene

2017· article· en· W2594553466 on OpenAlexafffund
Johanne Tremblay, Yujia Wang, John Raelson, François-Christophe Marois-Blanchet, Zenghui Wu, Hongyu Luo, Edward C. Bradley, John Chalmers, Mark Woodward, Stephen Harrap, Pavel Hamet, Jiangping Wu

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldNeuroscience
TopicAxon Guidance and Neuronal Signaling
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsSingle-nucleotide polymorphismGeneBiologyErythropoietin-producing hepatocellular (Eph) receptorGeneticsBlood pressureDiabetes mellitusEphrinBioinformaticsMedicineGenotypeInternal medicineEndocrinologyKinaseReceptor

Abstract

fetched live from OpenAlex

EPH kinases and their ligands, ephrins (EFNs), have vital and diverse biological functions. We recently reported that Efnb3 gene deletion results in hypertension in female but not male mice. These data suggest that EFNB3 regulates blood pressure in a sex- and sex hormone-dependent way. In the present study, we conducted a human genetic study to assess the association of EFNB3 single nucleotide polymorphisms with human hypertension risks, using 3,448 patients with type 2 diabetes from the ADVANCE study (Action in Diabetes and Vascular Disease: Peterax and Diamicron MR Controlled Evaluation). We have observed significant association between 2 SNPs in the 3' untranslated region or within the adjacent region just 3' of the EFNB3 gene with hypertension, corroborating our findings from the mouse model. Thus, our investigation has shown that EFNB3 is a hypertension risk gene in certain individuals.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.153
GPT teacher head0.348
Teacher spread0.194 · 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 designObservational
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

Citations9
Published2017
Admission routes2
Has abstractyes

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