Specificity in the Susceptibilities of Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus Clinical Isolates to Six Metal Antimicrobials
Bibliographic record
Abstract
In response to the occurrence of antibiotic resistance, there has been rapid developments in the field of metal-based antimicrobials. Although it is largely assumed that metals provide broad-spectrum microbial efficacy, studies have shown that this is not always the case. Therefore, in this study, we compared the susceptibilities of 93 clinical isolates belonging to the species Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus against six metals, namely aluminum, copper, gallium, nickel, silver and zinc. To provide qualitative comparative information, the resulting zones of growth inhibition were compared to the minimal inhibitory concentrations of three indicator strains E. coli ATCC 25922, P. aeruginosa ATCC 27853 and S. aureus ATCC 25923. Here, we demonstrate that the metal efficacies were species- and isolate-specific. Only several isolates were either resistant or sensitive to all of the six metals, with great variability found between isolates. However, the greatest degree of similarity was found with the E. coli isolates. In contrast, the susceptibilities of the remaining two collections, S. aureus and P. aeruginosa, were more highly dispersed. Using this information, we have shown that metals are not equal in their efficacies. Hence, their use should be tailored against a particular microorganism and care should be taken to ensure the use of the correct concentration.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".