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Method for the Fabrication of Elastomeric Polyester Scaffolds for Tissue Engineering and Minimally Invasive Delivery

2018· article· en· W2788532975 on OpenAlexafffund
Miles Montgomery, Locke Davenport Huyer, Dawn Bannerman, Mohammad Hossein Mohammadi, Genevieve Conant, Milica Radisic

Bibliographic record

VenueACS Biomaterials Science & Engineering · 2018
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchOntario Institute for Regenerative MedicineUniversity of TorontoCanada Foundation for InnovationHeart and Stroke Foundation of Canada
KeywordsScaffoldMaterials scienceElastomerTissue engineeringPolyesterMaleic anhydridePolymerBiomedical engineeringFabricationNanotechnologyMonomerCopolymerComposite material

Abstract

fetched live from OpenAlex

Using the methods described herein, we have demonstrated how scaffolds can be designed for a number of applications including tissue engineering, biomedical devices and injectable tissues. Details on the methods of polymerization and physical and chemical characterization of poly(octamethylene maleate (anhydride) citrate (POMaC) are described. Two POMaC polymer recipes with different monomer ratios of maleic anhydride and citric acid were synthesized and compared. Mechanical testing was performed on scaffolds of two distinct anisotropic designs to show how scaffold design influences the apparent elasticity in the long and short axis. POMaC scaffolds of various patterns and geometries were fabricated to demonstrate: (1) scaffold function can be determined by scaffold design (e.g., inherent shape-memory or self-assembling tubular structures), and (2) the soft lithography approach to fabricating biodegradable elastomers described here can be used to suit a number of different potential applications.

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)0.000
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.004

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 head0.016
GPT teacher head0.294
Teacher spread0.278 · 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

Citations28
Published2018
Admission routes2
Has abstractyes

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Same venueACS Biomaterials Science & EngineeringSame topic3D Printing in Biomedical ResearchFrench-language works237,207