The use of mechanical foaming for development of bioinks and bioprinting techniques
Notice bibliographique
Résumé
Three-dimensional bioprinting is a unique subset of additive manufacturing in which the precise deposition of bioink gives yield to complex 3D structures made up of living cells embedded within hydrogels. For biological applications, 3D bioprinted structures are required to have a high degree of porosity to promote cellular growth and function. However, the low intrinsic porosity of bioinks often falls short in providing an ideal environment for cells. Moreover, many biocompatible hydrogels lack sufficient mechanical and rheological properties for 3D bioprinting. When these properties do not meet the requirements for printability, bioprinting fails due to poor shape fidelity and other complications. Therefore, further attention is required to develop methods to address the aforementioned limitations. \n \nThis thesis addresses the development of foam-based methods for the 3D bioprinting of supermacroporous structures, as well as the 3D bioprinting of low-viscosity/slow-crosslinking hydrogels. Albumin, a well-known foaming agent, is used to produce highly porous and 3Dprintable hybrid bioinks. Moreover, albumin foam is utilized as a sacrificial material to fabricate hollow fibers, and also is used as a foam-based support material in embedded bioprinting as an alternative to gel-based support baths. \n \nFor the investigation of a porous 3D-printable bioink, hybrid albumin-alginate solutions with various concentrations of albumin and sodium alginate are prepared and mechanically mixed. The resulting foam is 3D printed and crosslinked with a calcium chloride solution or mist. The fabricated scaffolds are characterized through the analysis of the printability, mechanical properties, porosity, water absorption, degradation, and drug release tests. These studies indicate that the mist-crosslinked scaffolds show superior mechanical properties and provide relatively longer drug release profiles. \n \nFor the hollow fiber bioprinting study, a foam-based sacrificial material is developed to be used as a core flow. This method involves the incorporation of the crosslinker (calcium chloride) into an albumin foam. The effects of foam and alginate flow rates on fiber diameter and wall thickness are investigated. Printing resolution is quantified by determining the printability number. Mechanical properties are assessed by analyzing breaking strain and filament collapse. Moreover, the viability of Neuro-2a cells co-incubated with the printed structures are examined, showing no detrimental impact on cell viability. \n \nFor the embedded bioprinting study, albumin foam enriched with cell culture media are used as a support bath to bioprint complex structures with low-viscosity/slow-crosslinking hydrogels. The optimal conditions of the foam-based bath are determined by testing different albumin concentrations and foaming times. A unique benefit of utilizing foam-based support baths in embedded bioprinting is the coalescence of bubbles over time, which leads to the formation of a sacrificial support bath. Additional benefits of utilizing foam as the supporting material include the enhanced access to surrounding gaseous oxygen, as well as immediate access to nutrients in the foam. A chitosan-based thermosensitive hydrogel of various complex structures are successfully bioprinted using the foam-based support material. Furthermore, the biocompatibility of the process is demonstrated by determining the cell viability over 7 days of L929 fibroblasts incorporated into a chitosan-collagen bioink. \n \nThe developed foam-based methods in this thesis leverage the advantages of the bioprinting techniques, while allowing direct 3D printing of highly porous structures, as well as lowviscosity/slow-crosslinking hydrogels. The use of innovative foam-based support materials is proved to be an excellent alternative to conventional support materials in coaxial bioprinting and embedded bioprinting systems for tissue engineering applications.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».