Anti-biofilm activity of murepavadin against cystic fibrosis <i>Pseudomonas aeruginosa</i> isolates
Bibliographic record
Abstract
OBJECTIVES: To determine the activity of murepavadin in comparison with tobramycin, colistin and aztreonam, against cystic fibrosis (CF) Pseudomonas aeruginosa isolates growing in biofilms. The biofilm-epidemiological cut-off (ECOFF) values that include intrinsic resistance mechanisms present in biofilms were estimated. METHODS: Fifty-three CF P. aeruginosa isolates from respiratory samples were tested using the Calgary (closed system) device, while 4 [2 clinical (one smooth, one mucoid) and 2 reference strains] were tested using the BioFlux, a microfluidic open model of biofilm testing. Biofilm was stained with SYTO9® and propidium iodide. The minimal biofilm inhibitory concentration (MBIC) and the minimal biofilm eradication concentration (MBEC) were determined. The MBIC-ECOFF and the MBEC-ECOFF were calculated. RESULTS: Colistin, tobramycin and murepavadin presented similar MBIC50/MBIC90 values (4/32, 8/64 and 2/32, respectively). Murepavadin exhibited the lowest MBEC90 (64 mg/L). Aztreonam MBIC and MBEC values were higher than those of the other antibiotics tested. Tobramycin and murepavadin had the lowest MBEC-ECOFF (64 and 128 mg/L, respectively), while those of aztreonam and colistin exceeded 512 mg/L. Using the BioFlux, for the PAO1, PAO mutS and the smooth clinical strain, a significant difference (P < 0.0125) was observed when comparing the fluorescence of treated and untreated biofilms. For the mucoid strain, only the biofilm treated with aztreonam (MBIC and MBEC) and tobramycin (MBEC) showed differences with respect to the untreated biofilm. CONCLUSIONS: Murepavadin demonstrated good activity against P. aeruginosa biofilms both in open and closed systems. The MBIC-ECOFF and the MBEC-ECOFF are proposed as new parameters to estimate the activity of antibiotics on biofilms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".