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Record W2009590691 · doi:10.1159/000163766

Proteoglycan Synthesis by the Neointimal Smooth Muscle Cells Cultured from Rabbit Aortic Explants following De-Endothelialization

2008· article· en· W2009590691 on OpenAlexaff
Zhihe Li, M. Alavi, Fasahat Wasty, Zorina S. Galis, Sean Moore

Bibliographic record

VenuePathobiology · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProteoglycans and glycosaminoglycans research
Canadian institutionsMcGill University
Fundersnot available
KeywordsNeointimaEndotheliumExtracellular matrixProteoglycanCell biologyAortaChemistryVascular smooth muscleEndothelial stem cellAnatomyIn vitroBiologySmooth muscleInternal medicineEndocrinologyMedicineBiochemistryRestenosis

Abstract

fetched live from OpenAlex

Proteoglycans (PGs), the essential component of the extracellular matrix, are implicated in the pathogenesis of atherosclerosis. In an experimental model of injury, PGs accumulate in the neointimal tissue parallel with lipid deposition. However, it is still not clear whether the PG accumulation is from active smooth muscle cell (SMC) production or is a consequence of trapping within neointima covered by endothelium. To study the effect of endothelial injury on PG synthesis, SMCs were cultured from normal aorta (N-SMC), neointima covered by regenerated endothelium (W-SMC) and neointima without endothelium (B-SMC). Using [35S]-Na2SO4, as a precursor in an in vitro incubation, the kinetics of PG synthesis were determined. PG synthesis by all three cell types increases as a function of time. It is significantly higher in the SMCs cultured from endothelium-denuded aortic explants (W- and B-SMC) than N-SMC. This finding indicates that endothelial injury stimulates PG synthesis by SMCs.

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 score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.240
Teacher spread0.228 · 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

Citations5
Published2008
Admission routes1
Has abstractyes

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