Graphene Oxide/Elastin Multilayered Membranes for Bone Regeneration
Notice bibliographique
Résumé
Introduction and objectives: Bone regeneration remains a great challenge due to the complexity of tissue repair. In the past few decades, a common strategy for repairing bone defects is to employ a bone implant1. However, conventional bone substitutes, including metallic materials, bioactive ceramics, and polymers, fail to provide mechanical properties matching those of native bone and biological activity for bone regeneration. Many natural materials have developed superior mechanical properties because of the ordered structure. Nacre, as an example, has an excellent combination of strength and toughness due to its “brick and mortar” layered structure, which consists of highly aligned aragonite platelets connected by organic layers in between the platelets2. Previous works showed that graphene oxide (GO) is an ideal candidate for the “bricks” due to its two-dimensional structure along with the outstanding mechanical strength and modulus3. However, GO-based nacre structures showed in previous works mainly focused on the electronic conductivity or mechanical properties instead of biological properties3-5. As for the choice of “mortar”, it varied from synthetic polymers to natural polymers such as silk fibroin3, chitosan4, and cellulose6. Elastin, as a polymeric extracellular matrix protein, has rarely been explored as the “mortar” phase. In this work, we fabricated multilayered GO/elastin membranes by a facile evaporation approach. GO nanosheets as the “bricks” can provide high modulus and strength to the membrane. Another advantage of GO is that it can promote the osteogenic differentiation of human mesenchymal stem cells, which facilitates bone regeneration. However, the elasticity of the GO membrane is lower than human cortical bone8. So, we select elastin as the “mortar” since it is a natural protein that provides elasticity to connective tissues9. Also, elastin acts as the main apatite nucleator in medial calcification10, which implies that elastin could promote mineralization of the membrane. However, the problem with elastin is weak mechanical strength. By combining GO and elastin, we aim to develop nacre-mimetic membranes with bone-matching mechanical properties and enhanced bioactivity for bone regeneration. R esults and discussions: The microstructure characterization confirmed the resultant membranes possessed a “brick and mortar” layered microstructure (Figure 1a). The prepared membranes had an average thickness ranging from 25 to 30 µm. The addition of elastin improved the stability of the multilayered membranes in water, which is essential for bone regeneration. The addition of elastin increased the tensile strength and Young’s modulus of the membrane (Figure 1b and 1c). However, there was no significant improvement in the tensile strain (Figure 1d). 20 wt% incorporation of elastin membrane showed the maximum tensile strength (90.2 ± 9.6 MPa) and Young’s modulus (13.5 ± 0.4 GPa), which are comparable to those of the human cortical bone8. Immersion tests in simulated body fluid are ongoing to evaluate apatite formation as an indicator of bone-forming ability. Future in vitro studies with mouse bone marrow stem cells could investigate cell viability and osteogenic differentiation on the fabricated membranes. Conclusions: By mimicking the ordered structure of natural nacre, we successfully fabricated multilayered GO/elastin membranes by a simple evaporation method. Incorporation of elastin improved the membrane’s stability in water, tensile strength and Young’s modulus. Among different membrane compositions, 20 wt% elastin addition membrane showed the best mechanical properties. Future simulated body fluid immersion test and cell culture experiments will be performed to investigate the bone regeneration ability of GO/elastin membranes. References: 1. M. M. Stevens, Materials today, 2008, 11, 18-25. 2. J. Wang, Q. Cheng and Z. Tang, Chemical Society Reviews, 2012, 41, 1111-1129. 3. K. Hu, M. K. Gupta, D. D. Kulkarni and V. V. Tsukruk, Advanced Materials, 2013, 25, 2301-2307. 4. S. Wan, J. Peng, Y. Li, H. Hu, L. Jiang and Q. Cheng, ACS nano, 2015, 9, 9830-9836. 5. W. Cui, M. Li, J. Liu, B. Wang, C. Zhang, L. Jiang and Q. Cheng, Acs Nano, 2014, 8, 9511-9517. 6. J. Duan, S. Gong, Y. Gao, X. Xie, L. Jiang and Q. Cheng, ACS applied materials & interfaces, 2016, 8, 10545-10550. 7. W. C. Lee, C. H. Y. Lim, H. Shi, L. A. Tang, Y. Wang, C. T. Lim and K. P. Loh, ACS nano, 2011, 5, 7334-7341. 8. T. M. Keaveny, E. F. Morgan and O. C. Yeh, Standard handbook of biomedical engineering and design, 2004, 1-24. 9. B. Vrhovski and A. S. Weiss, European Journal of Biochemistry, 1998, 258, 1-18. 10. O. Gourgas Ophélie, Biomacromolecules, 2019, 20, 2625. Figure 1
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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».