Dispensing-based bioprinting of mechanically-functional hybrid scaffolds with vessel-like channels for tissue engineering applications – A brief review
Bibliographic record
Abstract
Over the past decades, significant progress has been achieved in the field of tissue engineering (TE) to restore/repair damaged tissues or organs and, in this regard, scaffolds made from biomaterials have played a critical role. Notably, recent advances in biomaterials and three-dimensional (3D) printing have enabled the manipulation of two or more biomaterials of distinct, yet complementary, mechanical and/or biological properties to form so-called hybrid scaffolds mimicking native tissues. Among various biomaterials, hydrogels synthesized to incorporate living cells and/or biological molecules have dominated due to their hydrated tissue-like environment. Moreover, dispensing-based bioprinting has evolved to the point that it can now be used to create hybrid scaffolds with complex structures. However, the complexities associated with multi-material bioprinting and synthesis of hydrogels used for hybrid scaffolds pose many challenges for their fabrication. This paper presents a brief review of dispensing-based bioprinting of hybrid scaffolds for TE applications. The focus is on the design and fabrication of hybrid scaffolds, including imaging techniques, potential biomaterials, physical architecture, mechanical properties, cell viability, and the importance of vessel-like channels. The key issues and challenges for dispensing-based bioprinting of hybrid scaffolds are also identified and discussed along with recommendations for future research directions. Addressing these issues will significantly enhance the design and fabrication of hybrid scaffolds to and pave the way for translating them into clinical applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".