Copper and Quaternary Ammonium Cations Exert Synergistic Bactericidal and Antibiofilm Activity against<i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Biofilms are slimy aggregates of microbes that are likely responsible for many chronic infections as well as for contamination of clinical and industrial environments. Pseudomonas aeruginosa is a prevalent hospital pathogen that is well known for its ability to form biofilms that are recalcitrant to many different antimicrobial treatments. We have devised a high-throughput method for testing combinations of antimicrobials for synergistic activity against biofilms, including those formed by P. aeruginosa. This approach was used to look for changes in biofilm susceptibility to various biocides when these agents were combined with metal ions. This process identified that Cu(2+) works synergistically with quaternary ammonium compounds (QACs; specifically benzalkonium chloride, cetalkonium chloride, cetylpyridinium chloride, myristalkonium chloride, and Polycide) to kill P. aeruginosa biofilms. In some cases, adding Cu(2+) to QACs resulted in a 128-fold decrease in the biofilm minimum bactericidal concentration compared to that for single-agent treatments. In combination, these agents retained broad-spectrum antimicrobial activity that also eradicated biofilms of Escherichia coli, Staphylococcus aureus, Salmonella enterica serovar Cholerasuis, and Pseudomonas fluorescens. To investigate the mechanism of action, isothermal titration calorimetry was used to show that Cu(2+) and QACs do not interact in aqueous solutions, suggesting that each agent exerts microbiological toxicity through independent biochemical routes. Additionally, Cu(2+) and QACs, both alone and in combination, reduced the activity of nitrate reductases, which are enzymes that are important for normal biofilm growth. Collectively, the results of this study indicate that Cu(2+) and QACs are effective combinations of antimicrobials that may be used to kill bacterial biofilms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".