Smart Dual‐Sensor Wound Dressing for Monitoring Cutaneous Wounds
Bibliographic record
Abstract
Managing slow-healing wounds and associated complications is challenging, time-consuming, and expensive. Systematic collection, analysis, and dissemination of correct wound status data are critical for enhancing healing outcomes and reducing complications. However, traditional data collection approaches are often neither accurate nor user-friendly and require diverse skill levels, resulting in the collection of inconsistent and unreliable data. As an advancement to the authors' previously developed hydrogel-based smart wound dressing, here is reported an enhanced integration of drug delivery and sensing (pH and glucose) modules for accelerated treatment and continuous monitoring of cutaneous wounds. In the current study, growth factor delivery modules and an array of colorimetric glucose sensors are incorporated into the dressing to promote wound healing and extend the dressing's utility for diabetic wound treatment. Furthermore, the efficacy of the wound dressing in monitoring infection and supporting wound healing via antibiotic and growth factor delivery is investigated in mice models. The updated dressing reveals excellent healing benefits on non-infected and infected wounds, as well as real-time monitoring and early detection of wound infection.
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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".