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Record W4309650920 · doi:10.1038/s42003-022-04243-6

Angiopoietin-like 2 is essential to aortic valve development in mice

2022· article· en· W4309650920 on OpenAlexafffund
Pauline Labbé, Victoria Munoz Goyette, Nathalie Thorin‐Trescases, Louis Villeneuve, Inès Desanlis, Constance Delwarde, Yanfen Shi, Cécile Martel, Carol Yu, Azadeh Alikashani, Maya Mamarbachi, Frédéric Lesage, Samuel Mathieu, Jean‐Claude Tardif, Patrick Mathieu, Marie Kmita, Éric Thorin

Bibliographic record

VenueCommunications Biology · 2022
Typearticle
Languageen
FieldMedicine
TopicLipid metabolism and disorders
Canadian institutionsPolytechnique MontréalMontreal Clinical Research InstituteUniversité de MontréalUniversité LavalMontreal Heart Institute
FundersCanadian Institutes of Health ResearchInstitut de Cardiologie de MontréalNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsAortic valveCardiologyInternal medicineAngiopoietin 2Medicine

Abstract

fetched live from OpenAlex

Aortic valve (AoV) abnormalities during embryogenesis are a major risk for the development of aortic valve stenosis (AVS) and cardiac events later in life. Here, we identify an unexpected role for Angiopoietin-like 2 (ANGPTL2), a pro-inflammatory protein secreted by senescent cells, in valvulogenesis. At late embryonic stage, mice knocked-down for Angptl2 (Angptl2-KD) exhibit a premature thickening of AoV leaflets associated with a dysregulation of the fine balance between cell apoptosis, senescence and proliferation during AoV remodeling and a decrease in the crucial Notch signalling. These structural and molecular abnormalities lead toward spontaneous AVS with elevated trans-aortic gradient in adult mice of both sexes. Consistently, ANGPTL2 expression is detected in human fetal semilunar valves and associated with pathways involved in cell cycle and senescence. Altogether, these findings suggest that Angptl2 is essential for valvulogenesis, and identify Angptl2-KD mice as an animal model to study spontaneous AVS, a disease with unmet medical need.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.838
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.324
Teacher spread0.295 · 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 designNot applicable
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
Published2022
Admission routes2
Has abstractyes

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