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Record W4409311032 · doi:10.1016/j.jacbts.2025.01.018

Transcriptomic Signatures of Calcific Aortic Valve Stenosis Severity in Human Tricuspid and Bicuspid Aortic Valves

2025· article· en· W4409311032 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJACC Basic to Translational Science · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsCardiologyInternal medicineBicuspid aortic valveStenosisMedicineAortic valveAortic valve stenosisBicuspid valve

Abstract

fetched live from OpenAlex

There is currently no medical therapy for calcific aortic valve stenosis. We aimed to identify transcriptomic signatures of the disease severity. We performed mRNA sequencing from explanted human aortic valves of 500 individuals who underwent aortic valve replacement (n = 440) or a heart transplant (n = 60). We performed differential gene expression analyses according to hemodynamic severity, valve morphology, calcification grade, and age of onset, and we estimated immune cell proportions. We identified immune response, inflammation, and adipocyte metabolism as predominant pathways associated with calcific aortic valve stenosis severity, offering new perspectives for the development of pharmacological treatments.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.339
Teacher spread0.326 · 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