MétaCan
Menu
Back to cohort
Record W2164655618 · doi:10.1002/mabi.201500130

Design, Degradation Mechanism and Long‐Term Cytotoxicity of Poly(<scp>l</scp>‐lactide) and Poly(Lactide‐co‐ϵ‐Caprolactone) Terpolymer Film and Air‐Spun Nanofiber Scaffold

2015· article· en· W2164655618 on OpenAlexafffund
Gad Sabbatier, Aitor Larrañaga, Andrée‐Anne Guay‐Bégin, Jorge Fernández, Florence Diéval, Bernard Durand, José-Ramon Sarasua, Gaétan Laroche

Bibliographic record

VenueMacromolecular Bioscience · 2015
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversité LavalHôpital Saint-François d'Assise
FundersNatural Sciences and Engineering Research Council of CanadaCentre québécois sur les matériaux fonctionnels
KeywordsCaprolactoneScaffoldPolyesterNanofiberContext (archaeology)BiomaterialLactideCopolymerMaterials scienceBiodegradable polymerDegradation (telecommunications)ElectrospinningPolymerPolymer chemistryChemistryNanotechnologyBiomedical engineeringComposite material

Abstract

fetched live from OpenAlex

Degradable nanofiber scaffold is known to provide a suitable, versatile and temporary structure for tissue regeneration. However, synthetic nanofiber scaffold must be properly designed to display appropriate tissue response during the degradation process. In this context, this publication focuses on the design of a finely-tuned poly(lactide-co-ϵ-caprolactone) terpolymer (PLCL) that may be appropriate for vascular biomaterials applications and its comparison with well-known semi-crystalline poly(l-lactide) (PLLA). The degradation mechanism of polymer film and nanofiber scaffold and endothelial cells behavior cultured with degradation products is elucidated. The results highlights benefits of using PLCL terpolymer as vascular biomaterial compared to PLLA.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.254
Teacher spread0.238 · 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
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

Citations31
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueMacromolecular BioscienceSame topicElectrospun Nanofibers in Biomedical ApplicationsFrench-language works237,207