Development of 3D printable graphene oxide based bio-ink for cell support and tissue engineering
Bibliographic record
Abstract
Tissue engineered constructs can serve as in vitro models for research and replacement of diseased or damaged tissue. As an emerging technology, 3D bioprinting enables tissue engineering through the ability to arrange biomaterials and cells in pre-ordered structures. Hydrogels, such as alginate (Alg), can be formulated as inks for 3D bioprinting. However, Alg has limited cell affinity and lacks the functional groups needed to promote cell growth. In contrast, graphene oxide (GO) can support numerous cell types and has been purported for use in regeneration of bone, neural and cardiac tissues. Here, GO was incorporated with 2% (w/w) Alg and 3% (w/w) gelatin (Gel) to improve 3D printability for extrusion-based 3D bioprinting at room temperature (RT; 25°C) and provide a 3D cellular support platform. GO was more uniformly distributed in the ink with our developed method over a wide concentration range (0.05%–0.5%, w/w) compared to previously reported GO containing bioink. Cell support was confirmed using adipose tissue derived stem cells (ADSCs) either seeded onto 3D printed GO scaffolds or encapsulated within the GO containing ink before direct 3D printing. Added GO was shown to improve cell-affinity of bioinert biomaterials by providing more bioactive moieties on the scaffold surface. 3D cell-laden or cell-seeded constructs showed improved cell viability compared to pristine (without GO) bio-ink-based scaffolds. Our findings support the application of GO for novel bio-ink formulation, with the potential to incorporate other natural and synthetic materials such as chitosan and cellulose for advanced in situ biosensing, drug-loading and release, and with the potential for electrical stimulation of cells to further augment cell function.
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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) | 0.000 | 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".