Advancing collagen-based biomaterials for oral and craniofacial tissue regeneration
Bibliographic record
Abstract
Abstract The oral and craniofacial region consists of various types of hard and soft tissues with the intricate organization. With the high prevalence of tissue defects in this specific region, it is highly desirable to enhance tissue regeneration through the development and use of engineered biomaterials. Collagen, the major component of tissue extracellular matrix, has come into the limelight in regenerative medicine. Although collagen has been widely used as an essential component in biomaterial engineering owing to its low immunogenicity, high biocompatibility, and convenient extraction procedures, there is a limited number of reviews on this specific clinic sector. The need for mechanical enhancement and functional engineering drives intensive efforts in collagen-based biomaterials concentrating on therapeutical outcomes and clinical translation in oral and craniofacial tissue regeneration. Herein, we highlighted the status quo of the design and applications of collagen-based biomaterials in oral and craniofacial tissue reconstruction. The discussion expanded on the inspiration from the leather tanning process on modifications of collagen-based biomaterials and the prospects of multi-tissue reconstruction in this particular dynamic microenvironment. The existing findings will lay a new foundation for the optimization of current collagen-based biomaterials for rebuilding oral and craniofacial tissues in the future. Graphical Abstract
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".