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Record W1969563792 · doi:10.1002/mabi.200600242

Preparation and Characterization of a Scaffold for Vascular Tissue Engineering by Direct‐Assembling of Collagen and Cells in a Cylindrical Geometry

2007· article· en· W1969563792 on OpenAlexaff
Francesca Boccafoschi, Navneeta Rajan, Jason Habermehl, Diego Mantovani

Bibliographic record

VenueMacromolecular Bioscience · 2007
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsScaffoldTissue engineeringBiomedical engineeringScanning electron microscopeExtracellular matrixMatrix (chemical analysis)Materials scienceChemistryAnatomyNanotechnologyComposite materialBiologyEngineeringBiochemistry

Abstract

fetched live from OpenAlex

In the past 25 years, several efforts have been focused on developing vascular substitutes showing long-term patency when implanted in humans. In this study, we present our last findings in an ongoing project aiming to develop a tissue-regenerated blood vessel from collagen-based scaffolds. A collagen-based scaffold with adequate biological properties was developed by directly assembling collagen and cells in a cylindrical geometry. The technique mainly involves the use of a ventilated rotating tube allowing smooth muscle cells to grow in a cylindrical collagen matrix. Scanning electron microscopy (SEM), histology, and cell cycle analyses were carried out in order to assess the biological potential of the cell-based scaffold for further maturation. Compliance results showed that this technique allows cells to assemble in the collagen matrix, thus providing enough rigidity to the structure to be handled and mounted in a perfusion bioreactor for further growth and maturation.

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)0.001
metaresearch head score (Gemma)0.001
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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.004
GPT teacher head0.247
Teacher spread0.243 · 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

Citations62
Published2007
Admission routes1
Has abstractyes

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