The efficacy of different antimicrobial metals at preventing the formation of, and eradicating bacterial biofilms of pathogenic indicator strains.
Bibliographic record
Abstract
The emergence of multidrug resistant pathogens and the prevalence of biofilm-related infections have generated a demand for alternative antimicrobial therapies. Metals have not been explored in adequate detail for their capacity to combat infectious disease. Metal compounds can now be found in textiles, medical devices, and disinfectants – yet, we know little about their efficacy against specific pathogens. To help fill this knowledge gap, we report on the antimicrobial and antibiofilm activity of seven metals; silver, copper, titanium, gallium, nickel, aluminum and zinc against three bacterial strains, Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli. In order to evaluate the capacity of metal ions to prevent the growth of, and eradicate biofilms and planktonic cells, bacterial cultures were inoculated in the Calgary Biofilm Device (MBEC™) in the presence the metal salts. Copper, gallium, and titanium were capable of preventing planktonic and biofilm growth, and eradicating established biofilms of all tested strains. Further, we observed that the efficacies of the other tested metal salts displayed variable efficacy against the tested strains. Further, contrary to the enhanced resistance anticipated from bacterial biofilms, particular metal salts were observed to be more effective against biofilm communities versus planktonic cells. In this study, we have demonstrated that the identity of the bacterial strain must be considered prior to treatment with a particular metal ion. Consequently, as the use of metal ions as antimicrobial agents to fight multidrug resistant and biofilm related infections increases, we must aim for more selective deployment in a given infectious setting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".