Chitosan-Nanocellulose Composites for Regenerative Medicine Applications
Bibliographic record
Abstract
Regenerative medicine represents an emerging multidisciplinary field that brings together engineering methods and complexity of life sciences into a unified fundamental understanding of structure-property relationship in micro/nano environment to develop the next generation of scaffolds and hydrogels to restore or improve tissue functions. Chitosan has several unique physico-chemical properties that make it a highly desirable polysaccharide for various applications such as, biomedical, food, nutraceutical, agriculture, packaging, coating, etc. However, the utilization of chitosan in regenerative medicine is often limited due to its inadequate mechanical, barrier and thermal properties. Cellulosic nanomaterials (CNs), owing to their exceptional mechanical strength, ease of chemical modification, biocompatibility and favorable interaction with chitosan, represent an attractive candidate for the fabrication of chitosan/ CNs scaffolds and hydrogels. The unique mechanical and biological properties of the chitosan/CNs bio-nanocomposite make them a material of choice for the development of next generation bio-scaffolds and hydrogels for regenerative medicine applications. In this review, we have summarized the preparation method, mechanical properties, morphology, cytotoxicity/ biocompatibility of chitosan/CNs nanocomposites for regenerative medicine applications, which comprises tissue engineering and wound dressing 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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".