Multimaterial bioprinting and combination of processing techniques towards the fabrication of biomimetic tissues and organs
Bibliographic record
Abstract
Tissue reconstruction requires the utilization of multiple biomaterials and cell types to replicate the delicate and complex structure of native tissues. Various three-dimensional (3D) bioprinting techniques have been developed to fabricate customized tissue structures; however, there are still significant challenges, such as vascularization, mechanical stability of printed constructs, and fabrication of gradient structures to be addressed for the creation of biomimetic and complex tissue constructs. One approach to address these challenges is to develop multimaterial 3D bioprinting techniques that can integrate various types of biomaterials and bioprinting capabilities towards the fabrication of more complex structures. Notable examples include multi-nozzle, coaxial, and microfluidics-assisted multimaterial 3D bioprinting techniques. More advanced multimaterial 3D printing techniques are emerging, and new areas in this niche technology are rapidly evolving. In this review, we briefly introduce the basics of individual 3D bioprinting techniques and then discuss the multimaterial 3D printing techniques that can be developed based on combination of these techniques for the engineering of complex and biomimetic tissue constructs. We also discuss the perspectives and future directions to develop state-of-the-art multimaterial 3D bioprinting techniques for engineering tissues and organs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".