Recent Advances in Cellulose-Based Structures as the Wound-Healing Biomaterials: A Clinically Oriented Review
Bibliographic record
Abstract
Application of wound-healing/dressing biomaterials is amongst the most promising approaches for wound repair through protection from pathogen invasion/contamination, maintaining moisture, absorbing exudates, modulating inflammation, and facilitating the healing process. A wide range of materials are used to fabricate wound-healing/dressing biomaterials. Active wound-healing/dressings are next-generation alternatives for passive biomaterials, which provide a physical barrier and induce different biological activities, such as antibacterial, antioxidant, and proliferative effects. Cellulose-based biomaterials are particularly promising due to their tunable physical, chemical, mechanical, and biological properties, accessibility, low cost, and biocompatibility. A thorough description and analysis of wound-healing/dressing structures fabricated from cellulose-based biomaterials is discussed in this review. We emphasize and highlight the fabrication methods, applied bioactive molecules, and discuss the obtained results from in vitro and in vivo models of cellulose-based wound-healing biomaterials. This review paper revealed that cellulose-based biomaterials have promising potential as the wound-dressing/healing materials and can be integrated with various bioactive agents. Overall, cellulose-based biomaterials are shown to be effective and sophisticated structures for delivery applications, safe and multi-customizable dressings, or grafts for wound-healing 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".