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Record W1510500805

Abstract 15350: Progressive Aortic Dilatation is Regulated by the mir-17 Cluster

2014· article· en· W1510500805 on OpenAlexaff
Jie Wu, Jian Guo, Shuhong Li, Katherine Tsang, L. Tumiati, Carolyn M. David, Joanne Bos, Maral Ouzounian, Terrence M. Yau, Tirone E. David, Richard D. Weisel, Ren‐Ke Li

Bibliographic record

VenueCirculation · 2014
Typearticle
Languageen
FieldMedicine
TopicAortic Disease and Treatment Approaches
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineBicuspid aortic valveAortic aneurysmThoracic aortic aneurysmMarfan syndromemicroRNACardiologyAortic valveInternal medicineAortaGene
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Ascending aortic aneurysms dilate due to activation of matrix metalloproteinase (MMPs), and matrix disruption. MicroRNAs (miRNAs) may contribute to the pathogenesis of aortic dilatation, but the specific miRNAs responsible have not been determined. We aimed to identify the miRNAs associated with progressive aortic dilation in patients with Marfan syndrome and bicuspid aortic valve (BAV) aortopathy. Methods and Results: Aortic tissue samples (~1х1cm) were collected from the dilated aneurysmal segment and adjacent nondilated segment which appeared normal at aortic surgery. The nondilated segments represent the early stages of aortic matrix degradation (before dilatation) and the dilated segment represents the late stage after aortic remodeling. Fourteen sample pairs were collected from BAV patients and 8 pairs from Marfan patients. miRNA levels were determined by Affymetrix GeneChip miRNA 3.0 Array for miRNA profiling. miRNA expression in the nondilated were compared to the dilated segments. T...

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 score0.003
Threshold uncertainty score0.011

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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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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