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Record W2124158335 · doi:10.1586/14779072.2014.923756

Molecular biology of calcific aortic valve disease: towards new pharmacological therapies

2014· review· en· W2124158335 on OpenAlexafffund
Patrick Mathieu, Marie‐Chloé Boulanger, Rihab Bouchareb

Bibliographic record

VenueExpert Review of Cardiovascular Therapy · 2014
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsInstitut Universitaire de Cardiologie et de Pneumologie de QuébecUniversité Laval
FundersCanadian Institutes of Health Research
KeywordsMedicineAortic valveFibrosisMyofibroblastInflammationCalcificationPathologyCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Calcific aortic valve disease (CAVD) is a chronic process leading to fibrosis and mineralization of the aortic valve. Investigations in the last several years have emphasized that key underlying molecular processes are involved in the pathogenesis of CAVD. In this regard, the processing of lipids and their retention has been underlined as an important mechanism that triggers inflammation. In turn, inflammation promotes/enhances the mineralization of valve interstitial cells, the main cellular component of the aortic valve. On the other hand, transformation of valve interstitial cells into myofibroblasts and osteoblast-like cells is determined by several signaling pathways having reciprocal cross-talks. In addition, the mineralization of the aortic valve has been shown to rely on ectonucleotidase and purinergic signaling. In this review, the authors have highlighted key molecular underpinnings of CAVD that may have significant relevance for the development of novel pharmaceutical therapies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.044
GPT teacher head0.440
Teacher spread0.396 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations51
Published2014
Admission routes2
Has abstractyes

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