Multiple Compounds Secreted by <i>Pseudomonas aeruginosa</i> Increase the Tolerance of <i>Staphylococcus aureus</i> to the Antimicrobial Metals Copper and Silver
Bibliographic record
Abstract
Alternative antimicrobials, such as metals, are one of the methods currently used to help mitigate antibiotic resistance. Metal-based antimicrobials such as copper and silver are used currently both to prevent and to treat infections. Although the efficacy of these antimicrobials has been determined in single-species culture, bacteria rarely exist in a single-species group in the environment. Both Pseudomonas aeruginosa and Staphylococcus aureus are often found associated with each other in severe chronic infections displaying increased virulence and antibiotic tolerance. In this study, we determined that multiple compounds secreted by P. aeruginosa are able to increase the tolerance of S. aureus to both copper and silver. This work demonstrates the expansive chemical communication occurring in polymicrobial infections between bacteria.
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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) | 0.000 | 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) | 0.001 | 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".