Combining Collagen and Bioactive Glasses for Bone Tissue Engineering: A Review
Bibliographic record
Abstract
Collagen (COL), the most abundant protein in mammals, offers a wide range of attractive properties for biomedical applications which are the result of its biocompatibility and high affinity to water. However, due to the relative low mechanical properties of COL its applications are still limited. To tackle this disadvantage of COL, especially in the field of bone tissue engineering, COL can be combined with bioactive inorganic materials in a variety of composite systems. One of such systems is the collagen-bioactive glass (COL-BG) composite family, which is the theme of this Review. BG fillers can increase compressive strength and stiffness of COL-based structures. This article reviews the relevant literature published in the last 15 years discussing the fabrication of a variety of COL-BG composites. In vitro cell studies have demonstrated the osteogenic, odontogenic, and angiogenic potential of these composite systems, which has been confirmed by stimulating specific biochemical indicators of relevant cells. Bony integration and connective tissue vessel formation have also been studied by implantation of the composites in vivo. Areas of future research in the field of COL-BG systems, based on current challenges, and gaps in knowledge are highlighted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".