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Record W2070037821 · doi:10.1002/jbm.a.31314

Poly(butyl methacrylate‐<i>co</i>‐methacrylic acid) tissue engineering scaffold with pro‐angiogenic potential <i>in vivo</i>

2007· article· en· W2070037821 on OpenAlexaff
Mark Butler, Michael V. Sefton

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2007
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceMethacrylic acidScaffoldMethacrylateBiomaterialTissue engineeringBiomedical engineeringContext (archaeology)In vivoCopolymerPolymer chemistryChemical engineeringComposite materialPolymerNanotechnology

Abstract

fetched live from OpenAlex

A poly(butyl methacrylate-co-methacrylic acid) (BMA-MAA) scaffold was fabricated by an in situ polymerization solvent casting/particulate leaching technique. It displayed high porosity (85-90%), pore interconnectivity, and a pore size range of 100-650 microm. Compression testing of the scaffolds demonstrated a dependence of the compressive stiffness on several fabrication variables including the ratio of monomer to salt used during the polymerization, the degree of salt fusion, and the choice of alternative comonomers to BMA. Subcutaneous implantation of BMA-MAA scaffolds in mice revealed an increased level of histological angiogenesis in tissue invading the pores of the scaffold compared to a BMA control, consistent with the prediction that methacrylic acid (MAA) containing copolymer beads are angiogenic in a wound healing context. At postoperative day 21, the capillary density in the BMA-MAA scaffolds was 56 +/- 13/mm(2) as compared to 32 +/- 8/mm(2) for the BMA scaffolds. With further investigation, it is expected that this biomaterial capable of eliciting an angiogenic response will have widespread application 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)0.008
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 score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.306
Teacher spread0.284 · 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

Citations26
Published2007
Admission routes1
Has abstractyes

Explore more

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