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Record W4378085051 · doi:10.1002/app.54240

A novel foaming technique to develop functional open‐cell polylactic acid scaffolds for bone tissue engineering

2023· article· en· W4378085051 on OpenAlexafffund
Miada Abubaker Osman, Nick Virgilio, Mahmoud Rouabhia, Louis-Étienne Lorenzo, Frej Mighri

Bibliographic record

VenueJournal of Applied Polymer Science · 2023
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversité LavalPolytechnique MontréalAS Composite (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolylactic acidCopolymerMaterials scienceScanning electron microscopeCompression moldingTissue engineeringChemical engineeringLactideMolding (decorative)ScaffoldPolymerBiomedical engineeringComposite materialMold

Abstract

fetched live from OpenAlex

Abstract The aim of this study was to develop a solvent‐free polylactic acid (PLA) open‐cell porous scaffold for bone tissue engineering using compression molding and a new chemical foaming compound, that we named CFCO. The latter is composed of a blend of PLA and azodicarbonamide (ADA) as a chemical foaming agent, with the addition of chitosan‐grafted PLA (CS‐g‐PLA) copolymer at various concentrations. The novelty of this approach is that during the foaming, that is, during the decomposition of the ADA foaming agent, the CS‐g‐PLA copolymer is projected toward the surface of the pores and strongly adhere there, which was confirmed by scanning electron microscopy and confocal microscopy. The results showed that at 6.90 wt% of CS‐g‐PLA copolymer, the immobilization of the latter on the surface of the pores led to a 52% increase in cell proliferation compared to the pure PLA control sample and a 26% increase compared to scaffolds that had 10 wt% of CS‐g‐PLA copolymer dispersed throughout the entire PLA matrix.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.713
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.244
Teacher spread0.227 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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