Biofabrication of Cellulose‐based Hydrogels for Advanced Wound Healing: A Special Emphasis on 3D Bioprinting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Even with the current advancements in wound management, addressing most skin injuries and wounds continues to pose a significant obstacle for the healthcare industry. As a result, researchers are now focusing on creating innovative materials utilizing cellulose and its derivatives. Cellulose, the most abundant biopolymer in nature, has unique properties that make it a promising material for wound healing, such as biocompatibility, tunable physiochemical characteristics, accessibility, and low cost. 3D bioprinting technology has enabled the production of cellulose-based wound dressings with complex structures that mimic the extracellular matrix. The inclusion of bioactive molecules such as growth factors offers the ability to aid in promoting wound healing, while cellulose creates an ideal environment for controlled release of these biomolecules and moisture retention. The use of 3D bioprinted cellulose-based wound dressings has potential benefits for managing chronic wounds, burns, and painful wounds by promoting wound healing and reducing the risk of infection. This review provides an up-to-date summary of cellulose-based dressings manufactured by 3D bioprinting techniques by looking into wound healing biology, biofabrication methods, cellulose derivatives, and the existing cellulose bioinks targeted toward wound healing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it