Antibacterial effect of cerium oxide nanoparticle against Pseudomonas aeruginosa
Bibliographic record
Abstract
BACKGROUND: Antibiotics have been widely used for the treatment of bacterial infections for decades. However, the rapid emergence of antibiotic-resistant bacteria has created many problems with a heavy burden for the medical community. Therefore, the use of nanoparticles as an alternative for antibacterial activity has been explored. In this context, metal nanoparticles have demonstrated broad-spectrum antimicrobial activity. This study investigated the antimicrobial activity of naked cerium oxide nanoparticles dispersed in aqueous solution (CNPs) and surface-stabilized using Pseudomonas aeruginosa as a bacterial model. METHODS: Gelatin-polycaprolactone nanofibers containing CNPs (Scaffold@CNPs) were synthesized, and their effect on P. aeruginosa was investigated. The minimum inhibitory and bactericidal concentrations of the nanoparticls were determined in an ATCC reference strain and a clinical isolate strain. To determine whether the exposure to the nanocomposites might change the expression of antibiotic resistance, the expression of the genes shv, kpc, and imp was also investigated. Moreover, the cytotoxicity of the CNPs was assessed on fibroblast using flow cytometry. RESULTS: Minimum bactericidal concentrations for the ATCC and the clinical isolate of 50 µg/mL and 200 µg/mL were measured, respectively, when the CNPs were used. In the case of the Scaffold@CNPs, the bactericidal effect was 50 µg/mL and 100 µg/mL for the ATCC and clinical isolate, respectively. Interestingly, the exposure to the Scaffold@CNPs significantly decreased the expression of the genes shv, kpc, and imp. CONCLUSIONS: A concentration of CNPs and scaffold@CNPs higher than 50 μg/mL can be used to inhibit the growth of P. aeruginosa. The fact that the scaffold@CNPs significantly reduced the expression of resistance genes, it has the potential to be used for medical applications such as wound dressings.
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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".