Comparative Antimicrobial Properties of Sodium Borate and Carbonate and their Perborate and Percarbonate Counterparts
Bibliographic record
Abstract
Antimicrobial resistance (AMR) poses a significant challenge in wound management, particularly in ischemic and chronic wounds, which are prone to infection and where traditional treatments often fall short. In response to this need, the antibacterial activity of polycaprolactone (PCL) films, composited with sodium perborate and sodium percarbonate to provide controlled release of oxygen and reactive oxygen species, is compared in vitro and in vivo. Sustained antimicrobial action against both Gram‐positive and Gram‐negative bacteria is measured in vitro that allowed lower quantities to be used compared with the borate and carbonate counterparts sodium borate and carbonate. This effect is also observed in vivo, such that perborate formulations are effective at wound treatment using one‐tenth the borate concentration required in sodium borate formulations. Overall, sodium perborate‐loaded films significantly accelerate wound closure, reduce bacterial load, and enhance early‐phase wound healing, outperforming borate equivalent counterparts at equivalent loading levels. In addition to effectively inhibiting bacterial growth, these composites prevent biofilm formation in vitro. These findings suggest that perborate‐loaded polymeric films could be a powerful tool in advanced wound care, offering both potent antimicrobial effects and promotion of wound healing in complex clinical 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 | 0.000 | 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".