Subinhibitory Cefotaxime and Levofloxacin Concentrations Contribute to Selection of Pseudomonas aeruginosa in Coculture with Staphylococcus aureus
Bibliographic record
Abstract
Increasing evidence has demonstrated the polymicrobial characteristics of most chronic infections, and the frequent communications among bacterial pathogens result in many difficulties for clinical therapy. Exploring bacterial interspecific interaction during antibiotic treatment is an emerging endeavor that may facilitate the understanding of polymicrobial infections and the optimization of clinical therapies. Here, we investigated the interaction of cocultured P. aeruginosa and S. aureus with the intervention of commonly used antibiotics in clinic. We found that the application of subinhibitory concentrations of cefotaxime and levofloxacin can select P. aeruginosa in coculture with S. aureus by modulating P. aeruginosa QS regulation to enhance the production of antistaphylococcal metabolites in different ways. This study emphasizes the role of the QS system in the interaction of P. aeruginosa with other bacterial species and provides an explanation for the persistence and enrichment of P. aeruginosa in patients after antibiotic treatment and a reference for further clinical therapy.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".