Silver Antibacterial Synergism Activities with Eight Other Metal(loid)-Based Antimicrobials against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus
Bibliographic record
Abstract
The present study surveys potential antibacterial synergism effects of silver nitrate with eight other metal or metalloid-based antimicrobials (MBAs), including silver nitrate, copper (II) sulfate, gallium (III) nitrate, nickel sulfate, hydrogen tetrachloroaurate (III) trihydrate (gold), aluminum sulfate, sodium selenite, potassium tellurite, and zinc sulfate. Bacteriostatic and bactericidal susceptibility testing explored antibacterial synergism potency of 5760 combinations of MBAs against three bacteria (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus) in three different media. Silver nitrate in combination with potassium tellurite, zinc sulfate, and tetrachloroaurate trihydrate had remarkable bactericidal and bacteriostatic synergism effects. Synergism properties of MBAs decreased effective antibacterial concentrations remarkably and bacterial cell count decreased by 8.72 log10 colony-forming units (CFU)/mL in E. coli, 9.8 log10 CFU/mL in S. aureus, and 12.3 log10 CFU/mL in P. aeruginosa, compared to each MBA alone. Furthermore, most of the MBA combinations inhibited the recovery of bacteria; for instance, the combination of silver nitrate–tetrachloroaurate against P. aeruginosa inhibited the recovery of bacteria, while three-fold higher concentration of silver nitrate and two-fold higher concentration of tetrachloroaurate were required for inhibition of recovery when used individually. Overall, higher synergism was typically obtained in simulated wound fluid (SWF) rather than laboratory media. Unexpectedly, the combination of A silver nitrate–potassium tellurite had antagonistic bacteriostatic effects in Luria broth (LB) media for all three strains, while the combination of silver nitrate–potassium tellurite had the highest bacteriostatic and bactericidal synergism in SWF. Here, we identify the most effective antibacterial MBAs formulated against each of the Gram-positive and Gram-negative pathogen indicator strains.
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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) | 0.001 | 0.001 |
| 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".