Graphene Oxide/Elastin Multilayered Membranes for Bone Regeneration
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".