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Record W2113953028 · doi:10.1002/jbm.a.32244

Smooth muscle cell adhesion in surface‐modified three‐dimensional copolymer scaffolds prepared from co‐continuous blends

2008· article· en· W2113953028 on OpenAlexaff
Hanna Bramfeldt, Pierre Sarazin, Patrick Vermette

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2008
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceTissue engineeringCopolymerScaffoldComposite materialChemical engineeringPolystyreneBiomedical engineeringPolymer

Abstract

fetched live from OpenAlex

In this article, tissue-engineering scaffolds were fabricated from P(epsilon-CL-co-D,L-LA)-PEG-P(epsilon-CL-co-D,L-LA) copolymers using co-continuous blends with polystyrene as the porogen phase. By means of static annealing and following extraction of the porogen phase, pore sizes (channel widths) in the range of 15-350 microm were obtained. Smooth muscle cells were seeded in three-dimensional fibronectin-modified scaffolds of two different pore sizes. Considerably enhanced cell seeding efficiency was found for scaffolds with larger pore sizes, indicating the importance of this parameter to promote effective cell intrusion into bulk materials. Compressive moduli ranged from 2.3 +/- 0.3 to 67 +/- 15 MPa and decreased with increasing pore size. The reverse trend was found for scaffold permeability (kappa), which ranged from 8.5 x 10(-16) to 6.7 x 10(-11) m(2). This was comparable with permeabilities previously reported for scaffolds with higher pore sizes and void volumes, but more irregular pore morphologies. Taken together, the results obtained in this study motivate further investigation for possible future applications in tissue engineering.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.293
Teacher spread0.254 · 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.

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

Citations10
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Biomedical Materials Research Part ASame topicBone Tissue Engineering MaterialsFrench-language works237,207