Increased susceptibility to azithromycin of <i>Pseudomonas aeruginosa</i> biofilms using RPMI 1640 testing media
Bibliographic record
Abstract
Azithromycin (AZM) is efficient for treatment of chronic Pseudomonas aeruginosa biofilm lung infections, despite of resistance in conventional susceptibility testing. It has been shown that planktonic P. aeruginosa are more susceptible to AZM when tested in RPMI 1640 medium. The aim of the study was to test the susceptibility to AZM of P. aeruginosa biofilms in LB vs RPMI 1640 media. We investigated the effect of AZM on planktonic and biofilms of (WT) P. aeruginosa (PAO1), the hypermutable (ΔmutS) and the antibiotic‐resistant phenotype(ΔnfxB) mutants. The effect of AZM on young and mature biofilms was investigated in the modified Calgary Biofilm Device by estimation of the minimal biofilm inhibitory concentration (MBIC). The AZM MBIC90 in LB/RPMI1640 on young biofilms treated for 24 h was 16/4 μg/mL for PAO1, 32/8 μg/mL for ΔmutS, and 256/16 μg/mL for ΔnfxB, while in mature biofilms was 256/2 μg/mL for PAO1 and ΔmutS and 16/1 μg/mL for ΔnfxB. The effect of AZM was improved when the treatment was prolonged to 72 h, supporting the intracellular accumulation of AZM. An increased susceptibility of P. aeruginosa biofilms to AZM was observed in RPMI 1640 than in LB medium. Our results might improve susceptibility testing and dosing of AZM for treatment of biofilm infections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".