Biomaterials in bone and mineralized tissue engineering using 3D printing and bioprinting technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it