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Record W4410083423 · doi:10.1016/j.xhgg.2025.100448

Aortic valve-specific genes dysregulated in calcific aortic valve stenosis as potential biomarkers and therapeutic targets

2025· article· en· W4410083423 on OpenAlexafffund
Pardis Zamani, Ursula Houessou, Hasanga D. Manikpurage, Zhonglin Li, Manel Dahmene, Nathalie Gaudreault, François Dagenais, Marie‐Annick Clavel, Philippe Pîbarot, Benoît J. Arsenault, Patrick Mathieu, Yohan Bossé, Sébastien Thériault

Bibliographic record

VenueHuman Genetics and Genomics Advances · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersCanadian Institutes of Health ResearchUniversité LavalFondation Institut Universitaire de Cardiologie et de Pneumologie de QuébecHeart and Stroke Foundation of Canada
KeywordsCardiologyStenosisInternal medicineAortic valveMedicineAortic valve stenosis

Abstract

fetched live from OpenAlex

Calcific aortic valve stenosis (CAVS) is the most frequent heart valve disease. Elucidating specific gene expression patterns in the aortic valve could provide new insights for understanding disease pathophysiology. We used local RNA sequencing data from 500 explanted human aortic valves to identify aortic valve-specific genes and compared their expression according to disease status and CAVS severity. We identified 100 specific protein-coding genes in the aortic valve compared to 45 other tissues from the Genotype-Tissue Expression (GTEx) project. Among them, 38 were differentially expressed in CAVS. Ten had a gradient of expression between severity levels and were central in a protein-protein interaction network, most of which were involved in extracellular matrix regulation or inflammation. Among the aortic valve-specific genes, four of the corresponding proteins had a significantly different plasma level in individuals with CAVS. These findings represent a robust foundation for the development of specific biomarkers and therapies for CAVS.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score1.000

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 science0.0000.000
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 head0.012
GPT teacher head0.311
Teacher spread0.299 · 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.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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