An Advanced Multifunctional Hydrogel‐Based Dressing for Wound Monitoring and Drug Delivery
Bibliographic record
Abstract
Wound management is a major global challenge and poses a significant financial burden to the healthcare system due to the rapid growth of chronic diseases such as diabetes, obesity, and aging population. The ability to detect pathogenic infections and release drug at the wound site is of the utmost importance to expedient patient care. Herein, this study presents an advanced multifunctional dressing (GelDerm) capable of colorimetric measurement of pH, an indicator of bacterial infection, and release of antibiotic agents at the wound site. This study demonstrates the ability of GelDerm to detect bacterial infections using in vitro and ex vivo tests with accuracies comparable to the commercially available systems. Wireless interfaces to digital image capture hardware such as smartphones serve as a means for quantitation and enable the patient to record the wound condition at home and relay the information to the healthcare personnel for following treatment strategies. Additionally, the dressing is integrated within commercially available patches and can be placed on the wound without chemical or physical irritation. This study demonstrates the ability of GelDerm to eradicate bacteria by the sustained release of antibiotics. The proposed technology holds great promise in managing chronic and acute injuries caused by trauma, surgery, or diabetes.
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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".