Biomaterials in bone and mineralized tissue engineering using 3D printing and bioprinting technologies
Bibliographic record
Abstract
Abstract This review focuses on recently developed printable biomaterials for bone and mineralized tissue engineering. 3D printing or bioprinting is an advanced technology to design and fabricate complex functional 3D scaffolds, mimicking native tissue for in vivo applications. We categorized the biomaterials into two main classes: 3D printing and bioprinting. Various biomaterials, including natural, synthetic biopolymers and their composites, have been studied. Biomaterial inks or bioinks used for bone and mineralized tissue regeneration include hydrogels loaded with minerals or bioceramics, cells, and growth factors. In 3D printing, the scaffold is created by acellular biomaterials (biomaterial inks), while in 3D bioprinting, cell-laden hydrogels (bioinks) are used. Two main classes of bioceramics, including bioactive and bioinert ceramics, are reviewed. Bioceramics incorporation provides osteoconductive properties and induces bone formation. Each biopolymer and mineral have its advantages and limitations. Each component of these composite biomaterials provides specific properties, and their combination can ameliorate the mechanical properties, bioactivity, or biological integration of the 3D printed scaffold. Present challenges and future approaches to address them are also discussed.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.004 | 0.003 |
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".