Efficacy of Wound Cleansers on Wound-Specific Organisms Using In Vitro and Ex Vivo Biofilm Models
Bibliographic record
Abstract
Biofilms are believed to be a source of chronic inflammation in non-healing wounds. PURPOSE: In this study, the pre-clinical anti-biofilm efficacy of several wound cleansers was examined using the Calgary minimum biofilm eradication concentration (MBEC) and ex vivo porcine dermal explant (PDE) models on Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus (MRSA), and Candida albicans biofilms. METHODS: A surfactant-based cleanser and antimicrobial-based cleansers containing ionic silver, hypochlorous acid (HOCl), sodium hypochlorite (NaOCl), and polyhexamethylene biguanide (PHMB) were tested on the MBEC model biofilms with a 10-minute application time. Select cleansers were then tested on the mature PDE biofilms with 10-minute applications followed by the application of cleanser-soaked gauze. The PDE model was further expanded to include single and daily applications of the cleansers to mimic daily and 72-hour dressing changes. RESULTS: In the MBEC model, PHMB- and HOCl-based cleansers reduced immature MRSA, C albicans, and P aeruginosa biofilm regrowth by > 3× when compared with silver, surfactant, and saline cleansers. The major differences could be elucidated in the PDE model in which, after daily application, 1 PHMB-based cleanser showed a statistically significant reduction (3-8 CFU/mL log reduction) in all mature biofilms tested, while a NaOCl-based cleanser showed significant reduction in 2 microorganisms (3-5 CFU/mL log reduction, P aeruginosa and MRSA).The other PHMB-based cleanser showed a statistically significant 3 log CFU/mL reduction in P aeruginosa. The remaining cleansers showed no statistically significant difference from the saline control. CONCLUSION: Results confirm that there are model-dependent differences in the outcomes of these studies, suggesting the importance of model selection for product screening. The results indicate that 1 PHMB-based cleanser was effective in reducing mature P aeruginosa, MRSA, and C albicans biofilms and that sustained antimicrobial presence was necessary to reduce or eliminate these mature biofilms.
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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".