<i>In vitro</i> characterization of 3D printed polycaprolactone/graphene oxide scaffolds impregnated with alginate and gelatin hydrogels for bone tissue engineering
Bibliographic record
Abstract
To achieve successful bone tissue engineering (BTE), it is necessary to fabricate a biomedical scaffold with appropriate structure as well as favorable composition. Despite a broad range of studies, this remains a challenge, highlighting the need for a better understanding of how structural features (e.g., pore size) and scaffold composition influence mechanical and physical properties, as well as cellular behavior. Therefore, the objective of this study was to characterize physical properties (swelling, degradation), mechanical properties (compressive modulus), and cellular behavior in relation to varying compositions (referred to composite and hybrid scaffolds) as well as varying pore sizes in three-dimensional (3D) printed scaffolds. Composite scaffolds were fabricated from polycaprolactone (PCL) and two different graphene oxide (GO) (3% and 9% (w/v)) concentrations. Additionally, hybrid scaffolds were fabricated by impregnating 3D printed scaffolds in a hydrogel blend of alginate/gelatin. Pore sizes of 400, 1000, and 1500 μm were investigated in this study to assess their effect on the scaffold properties. Our findings showed that swelling and degradation properties were enhanced by (I) the addition of GO as well as introduction of both hydrogel and highest concentration of GO (9% (w/v) GO) into the polymeric matrix of PCL, and (II) increasing the pore size within the scaffolds. Mechanical testing revealed that compressive elastic modulus increased with decreasing pore size, incorporation of GO, and increasing GO content into the matrix of PCL. Although our investigated scaffolds with various pore sizes did not show comparable elastic moduli to that of cortical bone, they exhibited an elastic modulus range (∼31-48 MPa) matching that of trabecular bone. The highest compressive modulus (∼48 MPa) was observed in scaffolds of PCL/9% (w/v) GO (composite scaffolds) with the pore size of 400 μm. Cell viability assay demonstrated high MG-63 cell survival (greater than 70%) in all composite and hybrid scaffolds (indicating scaffold biocompatibility) except PCL/3% (w/v) GO scaffolds. The findings of this study contribute to the field of BTE by providing scaffold design insights in terms of pore size and composition.
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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 | 0.000 | 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".