Nanomaterials in Craniofacial Tissue Regeneration: A Review
Bibliographic record
Abstract
Nanotechnology is an exciting and innovative field when combined with tissue engineering, as it offers greater versatility in scaffold design for promoting cell adhesion, proliferation, and differentiation. The use of nanomaterials in craniofacial tissue regeneration is a newly developing field that holds great potential for treating craniofacial defects. This review presents an overview of the nanomaterials used for craniofacial tissue regeneration as well as their clinical applications for periodontal, vascular (endodontics), cartilage (temporomandibular joint), and bone tissue regeneration (dental implants and mandibular defects). To enhance periodontal tissue regeneration, nanohydroxyapatite was used in conjunction with other scaffold materials, such as polylactic acid, poly (lactic-co-glycolic acid), polyamide, chitosan, and polycaprolactone. To facilitate pulp regeneration along with the revascularization of the periapical tissue, polymeric nanofibers were used to simulate extracellular matrix formation. For temporomandibular joint (cartilage) engineering, nanofibrous-type and nanocomposite-based scaffolds improved tissue growth, cell differentiation, adhesion, and synthesis of cartilaginous extracellular matrix. To enhance bone regeneration for dental implants and mandibular bone defects, nanomaterials such as nanohydroxyapatite composite scaffolds, nanomodified mineral trioxide aggregate, and graphene were tested. Although the scientific knowledge in nanomaterials is rapidly advancing, there remain many unexplored data regarding their standardization, safety, and interactions with the nanoenvironment.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".