Cellulose-based composite scaffolds for bone tissue engineering and localized drug delivery
Bibliographic record
Abstract
Natural bone constitutes a complex and organized structure of organic and inorganic components with limited ability to regenerate and restore injured tissues, especially in large bone defects. To improve the reconstruction of the damaged bones, tissue engineering has been introduced as a promising alternative approach to the conventional therapeutic methods including surgical interventions using allograft and autograft implants. Bioengineered composite scaffolds consisting of multifunctional biomaterials in combination with the cells and bioactive therapeutic agents have great promise for bone repair and regeneration. Cellulose and its derivatives are renewable and biodegradable natural polymers that have shown promising potential in bone tissue engineering applications. Cellulose-based scaffolds possess numerous advantages attributed to their excellent properties of non-toxicity, biocompatibility, biodegradability, availability through renewable resources, and the low cost of preparation and processing. Furthermore, cellulose and its derivatives have been extensively used for delivering growth factors and antibiotics directly to the site of the impaired bone tissue to promote tissue repair. This review focuses on the various classifications of cellulose-based composite scaffolds utilized in localized bone drug delivery systems and bone regeneration, including cellulose-organic composites, cellulose-inorganic composites, cellulose-organic/inorganic composites. We will also highlight the physicochemical, mechanical, and biological properties of the different cellulose-based scaffolds for bone tissue engineering applications.
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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) | 0.003 | 0.002 |
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".