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.
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".