3D-Printed Gelatin-Alginate Hydrogel Dressings for Burn Wound Healing: A Comprehensive Study
Bibliographic record
Abstract
Burn wound treatment is still a clinical challenge due to the severity of tissue damage and dehydration. Among various wound dressings, hydrogel materials have gained significant attention for burn wound treatment in clinical practice due to their soothing and moisturizing activity. In this study, 3D-printed dressings were fabricated using clinically relevant hydrogels for deep partial-thickness burn (PTB) wounds. Different ratios of gelatin and alginate mixture were 3D-printed and examined in terms of rheological behavior, shear thinning behavior, mechanical properties, degradation rate, and hydration activity to tune the hydrogel composition for best functionality. The cell-laden dressings were bioprinted to evaluate the effect of the gelatin: alginate ratio on the proliferation and growth of human dermal fibroblasts. The present findings confirm that the higher alginate content is associated with higher viscosity and Young’s modulus, while higher gelatin content is associated with faster degradation and higher cell viability. Together, the 3D-printed dressing with 75% gelatin and 25% alginate showed the best tradeoff between mechanical properties, hydration activity, and in vitro biological response. Findings from in vivo test using the most effective dressing showed the positive effect of 3D-printed porous pattern on wound healing, including faster wound closure, regenerated hair follicles, and non-traumatic dressing removal compared to the non-printed hydrogel with the same composition and the standard of care. Results from this research showed that 3D-printed dressings with an adequate gelatin: alginate ratio enhanced wound healing activity for up to 7 days of moisture retention on deep PTB wounds.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".