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Record W2212535041

Abstract 9518: Role for Mir-126 in Right Ventricle Failure in Pulmonary Arterial Hypertension

2014· article· en· W2212535041 on OpenAlexaff
François Potus, Simon Malenfant, Sandra Breuils Bonnet, Roxane Paulin, Pasquale Ferrero, Evangelos D. Michelakis, Steeve Provencher, Sébastien Bonnet

Bibliographic record

VenueCirculation · 2014
Typearticle
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversity of AlbertaUniversité Laval
Fundersnot available
KeywordsMedicineMicrocirculationCardiologyPulmonary hypertensionSkeletal muscleCD31Vascular resistanceVentricleAngiogenesisInternal medicinePathogenesisPathologyPulmonary arteryHeart failureExercise intoleranceHemodynamics
DOInot available

Abstract

fetched live from OpenAlex

Rationale: Pulmonary arterial hypertension (PAH) is characterized by 1) pulmonary vascular lesions leading to increased pulmonary vascular resistance; 2) right ventricle (RV) microcirculation rarefaction contributing to its failure and 3) decrease skeletal muscle perfusion sustaining exercise intolerance. There is growing evidence that impaired angiogenesis could be a common denominator to this triad of abnormalities. An impaired endothelial function is recognized as the major trigger for PAH, although the mechanism remains unclear. MicroRNAs have emerged as major players in PAH pathogenesis. Thus we hypothesized that miR-126, an endothelial-specific pro-angiogenic microRNA is downregulated in PAH contributing to the pulmonary, RV and skeletal muscle abnormalities. Methods and Results: By qRT-PCR, we showed that miR-126 is down regulated in lungs, RV and skeletal muscles of PAH patients (n=10 to 20; p<0.01) compared to non-PAH donors. This correlates with a reduction of microcirculation (CD31 staining; p<...

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.265
Teacher spread0.246 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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