Biomimetic Natural Biomaterial Nanocomposite Scaffolds: A Rising Prospect for Bone Replacement
Bibliographic record
Abstract
Biomimetic natural biomaterial (BNBM) nanocomposite scaffolds for bone replacement can reduce the rate of implant failure and the associated risks of post-surgical complications for patients. Traditional bone implants, like allografts, and autografts, have limitations, such as donor site morbidity and potential patient inflammation. Over two million bone transplant procedures are performed yearly, and success varies depending on the material used. This emphasizes the importance of developing new biomaterials for bone replacement. Innovative BNBM nanocomposites for modern bone fabrication can promote the colonization of the desired cellular components and provide the necessary mechanical properties. Recent studies have highlighted the advantages of BNBM nanocomposites for bone replacement; therefore, this review focuses on the application of cellulose, chitosan, alginates, collagen, hyaluronic acid, and synthetic polymers enhanced with nanoparticles for the fabrication of nanocomposite scaffolds used in bone regeneration and replacement. This work outlines the most up-to-date overview and perspectives of selected promising BNBM nanocomposites for bone replacement that could be used for scaffold fabrication and replace other biomorphic materials such as metallics, ceramics, and synthetic polymers in the future. In summary, the concluding remarks highlight the advantages and disadvantages of BNBM nanocomposites, prospects, and future directions for bone tissue regeneration and replacement.
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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 | 0.002 | 0.001 |
| 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".