Inhibition of Pseudomonas aeruginosa biofilm formation on copper-based thin foils
Bibliographic record
Abstract
The development of Healthcare-Associated Infections (HAIs) represents an increasing threat to patient health. In this context, Pseudomonas aeruginosa is responsible for various HAIs, determining about 20% of the infections in hospitalized patients, which makes it one of the most effective pathogens due to its strong ability to form biofilms. Using Cu-based materials as foils on high-touch surfaces can help to prevent and mitigate P. aeruginosa contamination in biohazardous settings. However, the antibiofilm properties of Cu-based surfaces against P. aeruginosa may vary due to frequent touches combined with indoor environmental exposure. The main aim of this study is to investigate the impact of accelerated ageing, mimicking a high-touch frequency by cyclic exposure to artificial sweat solution as well as to temperature and relative humidity variations, on the efficacy of Cu-based thin foils against P. aeruginosa biofilms. Three Cu-based materials (rolled and annealed Phosphorous High-Conductivity (PHC) Cu, Cu15Zn brass, and Cu18Ni20Zn nickel silver) were evaluated. The ageing process enhanced the antibiofilm properties, due to an increment in Cu ion release: aged PHC Cu and Cu15Zn exhibited the highest Cu ion release and hence the highest biofilm inhibition (decrease in colony forming unit (CFU)) in comparison to their pristine counterpart, while aged Cu18Ni20Zn displayed the lowest biofilm formation reduction, despite showing the highest aesthetic and morphological stability. The Cu-based surface, which highlited the highest biofilm formation inhibition due to accelerated ageing, was Cu15Zn.
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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".