Antibiofilm Efficacy of DispersinB<sup>®</sup> Wound Spray Used in Combination with a Silver Wound Dressing
Bibliographic record
Abstract
Chronic wounds including diabetic foot ulcers, pressure ulcers, and venous leg ulcers are a worldwide health problem. As the traditional methods of treatment have proven ineffective against chronic wounds involving biofilms, there is an unmet clinical need for developing products with an antibiofilm component that inhibits and/or disrupts biofilms and thus make the biofilm-embedded bacteria more susceptible to antimicrobial therapy. We developed a DispersinB® antibiofilm enzyme-based wound spray for treating chronic wounds in conjunction with an antimicrobial. Under in vitro conditions, the DispersinB® and Acticoat™ combination performed significantly better (P < 0.05) than Acticoat™ alone, indicating the synergy between the two compounds because of DispersinB® enhancing the antimicrobial activity of Acticoat™. Furthermore, DispersinB® wound spray enhanced the antimicrobial activity of Acticoat™ in a chronic wound mouse model of methicillin-resistant Staphylococcus aureus (MRSA) infection. Thus, this novel combination of DispersinB® and Acticoat™, an antimicrobial dressing, prompts clinical evaluation for potential applications in biofilm-based chronic wound management.
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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".