Three-Dimensional Printing Methods for Bioceramic-Based Scaffold Fabrication for Craniomaxillofacial Bone Tissue Engineering
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
Three-dimensional printing (3DP) technology has revolutionized the field of the use of bioceramics for maxillofacial and periodontal applications, offering unprecedented control over the shape, size, and structure of bioceramic implants. In addition, bioceramics have become attractive materials for these applications due to their biocompatibility, biostability, and favorable mechanical properties. However, despite their advantages, bioceramic implants are still associated with inferior biological performance issues after implantation, such as slow osseointegration, inadequate tissue response, and an increased risk of implant failure. To address these challenges, researchers have been developing strategies to improve the biological performance of 3D-printed bioceramic implants. The purpose of this review is to provide an overview of 3DP techniques and strategies for bioceramic materials designed for bone regeneration. The review also addresses the use and incorporation of active biomolecules in 3D-printed bioceramic constructs to stimulate bone regeneration. By controlling the surface roughness and chemical composition of the implant, the construct can be tailored to promote osseointegration and reduce the risk of adverse tissue reactions. Additionally, growth factors, such as bone morphogenic proteins (rhBMP-2) and pharmacologic agent (dipyridamole), can be incorporated to promote the growth of new bone tissue. Incorporating porosity into bioceramic constructs can improve bone tissue formation and the overall biological response of the implant. As such, employing surface modification, combining with other materials, and incorporating the 3DP workflow can lead to better patient healing outcomes.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it