Cell–scaffold interactions in tissue engineering for oral and craniofacial reconstruction
Bibliographic record
Abstract
Tissue engineering (TE) is critical in oral and craniofacial reconstruction. One of the most popular topics on the biomaterial-based tissue regeneration process may be the interaction between cells and scaffolds. An increasing number of studies have identified the variables affecting cell-scaffold interaction. The creation and investigation of new scaffolds for TE and regenerative medicine based on specific interactions have become possible owing to these findings. This review discusses the effects of various types of scaffold materials on cells in TE. Because the intrinsic properties of scaffolds are essential, the influence of the physical, chemical, mechanical, and biological characteristics of scaffold materials on cell-scaffold interaction that has been discovered in recent research is elaborated in this review. The components carried by scaffolds, the degradation process, and the role of degraded products in cell-scaffold interactions are examined. Further, the roles of cells, including cell types, functions, and adhesion mechanisms, and extracellular matrix are discussed. Finally, the latest research progress on cell-scaffold interactions among various engineered tissues or organs in the oral and craniofacial region is summarized. A deeper understanding of cell-scaffold interactions is anticipated to benefit the development of TE and regenerative medicine.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".