MétaCan
Menu
Back to cohort

Elastin biosynthesis: The missing link in tissue-engineered blood vessels

2006· review· en· W2130744701 on OpenAlex

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 · 2006
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsWestern University
Fundersnot available
KeywordsElastinTissue engineeringVascular smooth muscleBlood vesselVascular tissueExtracellular matrixAnatomySmooth muscleMedicineBiologyCell biologyPathologyBiomedical engineeringInternal medicine

Abstract

fetched live from OpenAlex

Nearly 20 years have passed since Weinberg and Bell attempted to make the first tissue-engineered blood vessels. Following this early attempt, vascular tissue engineering has emerged as one of the most promising approaches to fabricate orderly and mechanically competent vascular substitutes. In elastic and muscular arteries, elastin is a critical structural and regulatory matrix protein and plays an important and dominant role by conferring elasticity to the vessel wall. Elastin also regulates vascular smooth muscle cells activity and phenotype. Despite the great promise that tissue-engineered blood vessels have to offer, little research in the last two decades has addressed the importance of elastin incorporation into these vessels. Although cardiovascular tissue engineering has been reviewed in the past, very little attention has been given to elastin. Thus, this review focuses on the recent advances made towards elastogenesis and the challenges we face in the quest for appropriate functional vascular substitutes.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.002
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 head0.093
GPT teacher head0.390
Teacher spread0.297 · 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