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Design of a Perfusion Bioreactor Specific to the Regeneration of Vascular Tissues Under Mechanical Stresses

2005· article· en· W2071018332 on OpenAlexaff
Katia Bilodeau, Frédéric Couët, Francesca Boccafoschi, Diego Mantovani

Bibliographic record

VenueArtificial Organs · 2005
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBioreactorRegeneration (biology)ScaffoldBiomedical engineeringPerfusionTissue engineeringRigidity (electromagnetism)Materials scienceChemistryCell biologyBiologyComposite materialEngineeringMedicineCardiology

Abstract

fetched live from OpenAlex

The objective of this work was to design a bioreactor to stimulate the three-dimensional regeneration of arterial tissue on a cylindrical scaffold with a methodological approach. Once seeded, the scaffold is perfused internally and the externally with culture medium with two independent perfusion systems at different flow rates. The horizontal position and the rotation of the construct ensure the uniformity of the arterial growth and of the endothelial cell spreading. During cell culture, the parameters, such as internal flow and stretching of the vessel, can evolve gradually from the fetal stage to the adult stage. The bioreactor will also be useful for investigating the influence of mechanical stresses and strains on the properties of mature arteries (rigidity, burst strength, adhesion of endothelial cells, etc.).

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)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.027
GPT teacher head0.264
Teacher spread0.237 · 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
GenreMethods

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

Citations75
Published2005
Admission routes1
Has abstractyes

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