Dual-action hydrogel wound dressing for advanced wound care: Antibiotic-free microbial defense and exceptional mechanical resilience
Bibliographic record
Abstract
Microbial invasion can hinder skin injury healing. Prolonged antibiotic use may not suit allergic patients and raises antibiotic resistance concerns. Here, we report a dual-action hydrogel wound dressing (DAHWD) that includes resistance to bending and compression fractures and prevention of microbial invasion to promote healing without antibiotics. This innovative dressing integrates ε-poly-L-lysine (EPL) into a carboxymethyl cellulose (CMC) hydrogel. We examine the impact of adding EPL to the CMC hydrogel, finding that simultaneous chemical and physical crosslinking enhances the DAHWD, resulting in improved resistance to fractures by bending and compressive deformation compared to the hydrogel with only chemical crosslinking. The EPL-modified hydrogel exhibits exceptional antimicrobial properties and biofilm inhibition comparable to commercial silver dressings. In vitro analyses confirm the DAHWD's biocompatibility and fibroblast migration promotion, while in vivo assessments highlight its effectiveness in preventing microbial infection and facilitating wound healing. This study underscores the DAHWD's potential as an antibiotic-free solution for advanced wound care.
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.
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".