Insights into the Synergistic Antibacterial Activity of Silver Nitrate with Potassium Tellurite against <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Metals and/or metalloids could represent antimicrobial alternatives for industrial and clinical applications (e.g., surface coatings, livestock, and topical infection control) because of their great properties, such as good stability and long half-life. Silver is the most common antimicrobial metal, but resistance prevalence is high, and it can be toxic to the host above a certain concentration. We found that a silver-tellurite composition has antibacterial synergistic effect and that the combination is beneficial to the host. So, the efficacy and application of silver could increase by adding tellurite in the recommended concentration(s). We used different methods to evaluate the mechanism for how this combination can be so incredibly synergistic, leading to efficacy against antibiotic- and silver-resistant isolates. Our two main findings are that (i) both silver and tellurite mostly target the same pathways and (ii) the coapplication of silver with tellurite tends not to target new pathways but targets the same pathways with an amplified change.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".