Evaluating the Effect of Copper Nanoparticles in Inhibiting Pseudomonas aeruginosa and Listeria monocytogenes Biofilm Formation.
Bibliographic record
Abstract
BACKGROUND: Biofilm formation is a major virulence factor in different bacteria. Biofilms allow bacteria to resist treatment with antibacterial agents. The biofilm formation on glass and steel surfaces, which are extremely useful surfaces in food industries and medical devices, has always had an important role in the distribution and transmission of infectious diseases. OBJECTIVES: In this study, the effect of coating glass and steel surfaces by copper nanoparticles (CuNPs) in inhibiting the biofilm formation by Listeria monocytogenes and Pseudomonas aeruginosa was examined. MATERIALS AND METHODS: The minimal inhibitory concentrations (MICs) of synthesized CuNPs were measured against L. monocytogenes and P. aeruginosa by using the broth-dilution method. The cell-surface hydrophobicity of the selected bacteria was assessed using the bacterial adhesion to hydrocarbon (BATH) method. Also, the effect of the CuNP-coated surfaces on the biofilm formation of the selected bacteria was calculated via the surface assay. RESULTS: The MICs for the CuNPs according to the broth-dilution method were ≤ 16 mg/L for L. monocytogenes and ≤ 32 mg/L for P. aeruginosa. The hydrophobicity of P. aeruginosa and L. monocytogenes was calculated as 74% and 67%, respectively. The results for the surface assay showed a significant decrease in bacterial attachment and colonization on the CuNP-covered surfaces. CONCLUSIONS: Our data demonstrated that the CuNPs inhibited bacterial growth and that the CuNP-coated surfaces decreased the microbial count and the microbial biofilm formation. Such CuNP-coated surfaces can be used in medical devices and food industries, although further studies in order to measure their level of toxicity would be necessary.
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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) | 0.001 | 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".