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Record W2117315365 · doi:10.1002/jbm.a.30640

Radial artery as an autologous cell source for valvular tissue engineering efforts

2006· article· en· W2117315365 on OpenAlexaff
Danielle Johnston, Derek R. Boughner, Massimo Cimini, Kem A. Rogers

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2006
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsRobarts Clinical TrialsWestern University
Fundersnot available
KeywordsScaffoldTissue engineeringMaterials scienceBiomedical engineeringZymographyExtracellular matrixCell biologyCell cultureHeart valveCellMatrix metalloproteinaseMedicineBiologySurgeryInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

To create a viable tissue-engineered aortic valve, it is important to identify suitable autologous cell sources that may be seeded onto a biocompatible scaffold. This study focused on the radial artery (RA) as one possible source, investigated optimal culture conditions, and determined the usefulness of small intestinal submucosa (SIS) as a scaffold for tissue-engineering. Porcine RA cells were cultured on either two-dimensional (2D) 100-mm dishes or three-dimensional (3D) 1-cm(2) SIS sheets, producing cell-scaffold composites (CSCs). Both 2D and 3D cultures were maintained in either Medium 199 (M199) or endothelial growth media (EGM) to determine optimal growth conditions. Cellular phenotype and matrix metalloproteinase (MMP) profiles were determined by immunoblotting of cell lysates and zymography of conditioned media, respectively. Cellular invasion was analyzed immunohistochemically on CSC tissue sections. We show that the RA contains phenotypes consistent with those found in the normal aortic valve. EGM, compared with M199, promotes the invasion and remodeling of SIS by RA cells, which is crucial in the process of replacing the foreign tissue scaffold prior to implantation. To our knowledge, this is the first study to show that the RA is a suitable source for the generation of a tissue-engineered valve.

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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.581

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.344
Teacher spread0.317 · 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 teacher head, 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
Published2006
Admission routes1
Has abstractyes

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