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The role of perlecan in arterial injury and angiogenesis

2004· review· en· W2131796090 on OpenAlex
Amit Segev

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.

Bibliographic record

VenueCardiovascular Research · 2004
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProteoglycans and glycosaminoglycans research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPerlecanAngiogenesisHeparan sulfateRestenosisEndotheliumExtracellular matrixIn vivoIntimal hyperplasiaBasement membraneEndothelial stem cellCancer researchCell biologyProteoglycanMedicineBiologyImmunologyIn vitroHeparinInternal medicineSmooth muscleBiochemistryStent

Abstract

fetched live from OpenAlex

Perlecan is a large heparan sulfate proteoglycan (HSPG), which is a major component of the vessel wall. In relation to vascular biology, perlecan has been shown to be a potent inhibitor of smooth muscle cell (SMC) activity. In vivo experiments in animal models of arterial injury have shown that perlecan may inhibit thrombosis and intimal hyperplasia. On the other hand, perlecan has been shown to have opposing effects on endothelial cells (ECs), where it promotes in vitro and in vivo angiogenesis and plays an important role in mediating tumor growth. These diverse biological effects, or "the perlecan paradox", are discussed in this review paper. The properties of perlecan including inhibition of SMC activity and thrombosis while enhancing EC proliferation are ideal for the prevention of in-stent restenosis. Perlecan's pro-angiogenic effects may be used for the treatment of various ischemic diseases such as intractable coronary artery disease and peripheral vascular disease.

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)0.004
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: Review · Consensus signal: Review
Teacher disagreement score0.997
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.039
GPT teacher head0.363
Teacher spread0.324 · 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