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Enregistrement W4366146794 · doi:10.11159/nddte23.123

New Approaches to Improve PCL Performances as Biomaterial for 3D Printing Of Bone Scaffolds

2023· article· en· W4366146794 sur OpenAlexvenueno aff
G. Auriemma, C. Tommasino, C. Sardo, R.P. Aquino

Notice bibliographique

RevueProceedings of the World Congress on Recent Advances in Nanotechnology · 2023
Typearticle
Langueen
DomaineEngineering
ThématiqueBone Tissue Engineering Materials
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBiomaterial3D printingComputer scienceBiomedical engineeringMaterials scienceEngineeringComposite material

Résumé

récupéré en direct d'OpenAlex

Tissue-engineered scaffolds have become a strategic approach to repair or even replace injured tissues, overcoming some limitations of autografts, allografts, and xenografts.Most injuries involve bone tissues and, as bone plays critical functions in the human body, there is a special need for adequate bone grafts [1].Nowadays, 3D printing (3DP) technologies are greatly increasing the performance of bone scaffolds.3DP allows building, starting from a digital model, threedimensional objects of virtually any complex geometry and architecture with high resolution, precision, and repeatability, guaranteeing highly innovative personalized solutions for a specific patient or a patient group [2].Among the different 3DP techniques, Fused Filament Fabrication (FFF) is one of the most used for its high versatility, precision, feasibility, and cheapness [2].But even if 3DP-FFF technology has become increasingly refined, the selection of biomaterials suitable for printing has remained limited.To date, the choice is mainly constrained to synthetic biodegradable polyesters, such as polylactic acid (PLA), polyglycolic acid (PGA), polycaprolactone (PCL), and the corresponding copolymers.Among polyesters, PCL is one of the most widely used, as it is cheap and readily available, biocompatible, bioresorbable and FDA approved for bone TE applications, compatible with various polymers and additives, and easily processable via FFF [3].However, its hydrophobia, slow biodegradation, and lack of bioactivity often lead to the failure of the implant.Hence, in this work, we propose different strategies to keep the positive features of this polyester, while enhancing its critical physicochemical properties.To achieve this goal, two main approaches were explored: a) development of PCL based hybrid materials by blending with both organic and/or inorganic components, and b) production of a partially chemically modified PCL.For the first approach, different materials, both inorganic (nanohydroxyapatite) and organic (alginate, microcrystalline cellulose and inulin-grafted-poly(D,L)lactic acid grafted copolymer [4]) were tested for blending with PCL.For the second approach, PCL was subjected to α-carbon functionalization with pendent ammino groups to introduce reactive moieties on the backbone for the following bioconjugation with Arg-Gly-Asp (RGD) peptide.In both cases, the resulting PCL-based biomaterials were first extruded in form of filament via hot melt extrusion, and then 3D printed via FFF as macroporous scaffolds.Finally, all the scaffolds were characterized in terms of physicochemical, technological properties (size, morphology and structural characteristics, mechanical properties, degradation profile), and in vitro biological performance (hemolysis, cytotoxicity, cell viability, and osteogenic activity assays).Preliminary results confirm the validity of blending as an effective approach to obtain novel PCL-based hybrid biomaterials for 3DP of bone scaffolds.The 3D printed hybrid scaffolds showed size, 3D architecture and macroporosity values very close to those of the digital model, confirming the high precision and accuracy of FFF technology.Furthermore, all scaffolds retained the good mechanical properties and the biocompatibility of PCL (high hemocompatibility and adequate cytocompatibility), while the addition of blending materials allowed to successfully modulate critical properties such as wettability, surface roughness, swelling ability and in vitro biodegradation profile.Concerning the second approach, a novel PCL derivative, with pendent amino groups on the backbone, was successfully obtained, as confirmed by NMR and FTIR analysis.Further studies are in progress to verify its processability via FFF, both alone and in blend with other components, and to optimize its printing conditions.Furthermore, an in-depth technological and biological characterization of the obtained scaffolds will be performed, to assess the ability of biofunctionalization to improve material hydrophilicity, increase cell adhesion and modulate bone cell response.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,005

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,241
Écart entre enseignants0,221 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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Même revueProceedings of the World Congress on Recent Advances in NanotechnologyMême sujetBone Tissue Engineering MaterialsTravaux en français237 207