Development and optimization of a multifunctional cellulose-based hydrogel for enhanced crosslinking and tunability
Bibliographic record
Abstract
Donor site wounds arise from the extraction of healthy skin for grafting to treat extensive skin loss from burns, ulcers, or trauma. These wounds often face challenges such as elevated pain, infection, and slow healing. Current treatments, like Xeroform gauze dressings, are inadequate in managing moisture and pain effectively. This study introduces a novel photocrosslinkable hydrogel dressing designed to address these issues. Using methacrylated cellulose and chitosan derivatives, we created an interpenetrating polymer network that crosslinks rapidly within 1 min. With a methacrylation degree of around 30 %, the hydrogel's mechanical properties, swelling ratio, and rheological characteristics were optimized by adjusting the cellulose concentration. The optimal hydrogel demonstrated excellent hemocompatibility and no toxicity towards 3T3 fibroblast cells. Compared to a commercial dressing (Jelonet), it exhibited better antimicrobial properties without containing any antimicrobial agents and demonstrated remarkable antifouling properties against E. coli, preventing biofilm formation. This advanced hydrogel offers enhanced moisture control and potential for pain management, providing a promising solution for improved donor site wound care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".