3D bioprinting of thick core–shell vascularized scaffolds for potential tissue engineering applications
Bibliographic record
Abstract
• Scaffolds with embedded hollow channels were successfully 3D bioprinted. • Cells showed high survival rates after 3D bioprinting. • Endothelial cells formed lumen over time in the scaffolds. • Mechanical properties of the scaffolds were similar to soft tissues. The promise of tissue engineering in developing functional, living, 3D thick structures has been limited due to the constraints of nutrient and oxygen delivery through diffusion. Although advancements in additive manufacturing approaches have enhanced the fidelity and complexity of 3D (bio)printed constructs, the vascularization of such scaffolds is less investigated. Here, we have leveraged extrusion-based 3D bioprnting of core/shell constructs to develop millimeter-thick scaffolds with embedded microvasculature for potential soft tissue repair. Composites of methacrylated gelatin (GelMA) and gelatin have been used for this purpose. A systematic approach was used to investigate the effect of parameters, such as material and photoinitiator concentrations, and photocuring time, on the properties of constructs. Results have shown the structures have Young’s modulus close to the soft tissues. 3D bioprinting parameters were optimized so that the printing and photo crosslinking procedures did not negatively affect the cell viability. It was also observed that a continuous hollow inner core could be successfully printed within the scaffolds, which upon incorporation of endothelial cells during the 3D bioprinting process, could form micro-vessels embedded in the constructs. Together, our results demonstrate the significant potential of the proposed approach for developing thick vascularized tissue-engineered scaffolds suitable for soft tissue engineering.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".