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Record W2103201793 · doi:10.1161/hq0302.104529

Lack of Plasminogen Activator Inhibitor-1 Promotes Growth and Abnormal Matrix Remodeling of Advanced Atherosclerotic Plaques in Apolipoprotein E–Deficient Mice

2002· article· en· W2103201793 on OpenAlexaff
Aernout Luttun, Florea Lupu, Erik Storkebaum, Marc Hoylaerts, Lieve Moons, James T. B. Crawley, Françoise Bono, A. Robin Poole, Peter G. Tipping, Jean‐Marc Herbert, Désiré Collen, Peter Carmeliet

Bibliographic record

VenueArteriosclerosis Thrombosis and Vascular Biology · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsPlasminogen activator inhibitor-1Plasminogen activatorApolipoprotein ECancer researchApolipoprotein BChemistryCell biologyMedicineBiologyInternal medicineCholesterolDisease

Abstract

fetched live from OpenAlex

Epidemiological studies suggest that elevated plasma levels of plasminogen activator inhibitor-1 (PAI-1) predispose an individual to ischemic heart disease or promote plaque progression by inhibiting fibrinolysis. In the present study, loss of PAI-1 in apolipoprotein E (apoE)-deficient (apoE(-/-):PAI-1(-/-)) mice promoted the growth of advanced atherosclerotic plaques, which was due to enhanced extracellular matrix deposition. ApoE(-/-):PAI-1(-/-) plaques also exhibited collagen fiber disorganization and degradation. Immunostaining and bone marrow transplantation revealed that smooth muscle cells, not macrophages, primarily expressed PAI-1 in plaques. Thus, although PAI-1 may promote plaque growth because of its antifibrinolytic properties, the present study reveals a protective role for PAI-1 by limiting plaque growth and preventing abnormal matrix remodeling.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.033
GPT teacher head0.268
Teacher spread0.236 · 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 designBench or experimental
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

Citations124
Published2002
Admission routes1
Has abstractyes

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