Comparative study of common over-the-counter wound care products against early and mature biofilms of antibiotic-resistant wound pathogens
Bibliographic record
Abstract
Background: The global rise of antimicrobial resistance requires innovative and affordable wound care solutions. Moreover, managing wounds infected with priority pathogens remains a challenge. Despite the widespread availability of over-the-counter (OTC) antiseptics in wound care, comparative studies on their efficacy against biofilms or multidrug-resistant pathogens are limited. Objectives: and Methods: The antimicrobial activity of seven antiseptics (polyhexanide, octenidine, chloroxylenol, chlorhexidine, ethanol, cetrimide, phenol) against early-stage (3-hour) and mature (24-hour) biofilms was determined by measuring reductions in colony-forming units (cfu)/biofilm while varying treatment exposure time. Results: = 3, LOD = 100 cfu/biofilm). In mature biofilms, significant differences emerged. Polyhexanide, octenidine and cetrimide yielded modest reductions in cfu count/biofilm (0.55-0.64-log) after 5 minutes, while chloroxylenol and phenol achieved ∼2.5-log reductions; notably, chlorhexidine reduced cfu/mature biofilms below detectable limits within 5 minutes. Extended exposure (60 minutes) enhanced the efficacy of phenol and ethanol, with chloroxylenol and octenidine reducing cfu/biofilm below detectable limits. Conclusion: OTC antiseptics are effective in eliminating early-stage biofilms; however, mature biofilms require either prolonged exposure, which may increase their toxicity and delay wound healing, or the use of potent formulations. Chlorhexidine gluconate, chloroxylenol and phenol offer an optimal balance between antibiofilm potency and tissue safety, offering promise for acute and chronic wound management particularly in low-resource settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".