Antibacterial and biodegradable bandage with exudate absorption and smart monitoring for chronic wound management
Bibliographic record
Abstract
Despite the promising potential of smart bandages in wound care, the lack of effective integration among infection control, exudate management, and real-time wound monitoring remains a major obstacle in clinical application. Herein, neomycin (NEO)-grafted cellulose-based nonwovens (CNs) were used as the antibacterial network and blueberry extract (anthocyanin, AC) as the colorimetric additive to create the excellent dual network gel (DNG) bandage for smart bandages along with a polyvinyl alcohol/cellulose nanofiber (PVA/CNFs) matrix. The aerogel bandage loaded with AC demonstrates a pH-sensitive color-changing response and high-efficiency free radical scavenging ability (all greater than 93.61%), enabling the in-situ monitoring of wound healing and inhibiting wound inflammation, while the nonwoven network grafted with NEO endows the aerogel composites with excellent antibacterial properties (> 99% against Staphylococcus aureus and Escherichia coli ). In vivo evaluation using a S. aureus -infected full-thickness wound model in mice demonstrated that the DNG bandage significantly accelerated wound healing and improved tissue regeneration, outperforming commercial dressings. Furthermore, upon absorbing exudate, the aerogel converts into a hydrogel, providing efficient fluid absorption and preventing wound re-contamination, thereby achieving dynamic exudate management. Evidently, the DNG smart bandage is a promising management tool for the synergistic treatment of persistent wounds and introduces a fresh strategy for medical regenerative medicine.
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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".