Closed‐Loop Responsive Smart Bandages for Dynamic Monitoring and Self‐Regulated Antibacterial Therapy in Wound Healing
Bibliographic record
Abstract
Abstract Currently, smart wound management systems have garnered increasing attention. However, most existing strategies still rely on predefined stimuli or threshold‐based mechanisms to trigger drug release, which are insufficient to feedback to the dynamic changes in infection status, and their independently designed sensing and therapeutic modules further limit functional integration and coordinated intervention. To address these challenges, this study presents a smart bandage system that integrates a closed‐loop mechanism encompassing sensing, therapy intervention, and feedback. The system is built by embedding carbon dots (CDs) into metal–organic frameworks (MOFs) formed via Fe 3+ ‐carbenicillin (CARB) coordination, then immobilized on a cellulose nonwoven (CNW) to create a composite smart bandage. In the infection‐induced acidic microenvironment, the bandage enables concurrent fluorescence recovery and CARB release, enabling real‐time infection monitoring and potent antibacterial activity (>99.99%). As infection resolves, diminished bacterial activity raises local pH, thereby reducing CARB release through pH‐responsive negative feedback. Mechanistic studies indicate that Fe 3+ competitively coordinates between bacterial ligands and CDs, enabling coupled sensing and therapy. In a murine wound model, the bandage significantly accelerated wound healing, suppressed bacterial growth, and allowed visual tracking of infection severity. This work provides a novel strategy for an intelligent wound bandage with autonomous sensing and therapeutic functions.
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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".