A new approach to study attached biofilms and floating communities from Pseudomonas aeruginosa strains of various origins reveals diverse effects of divalent ions
Bibliographic record
Abstract
Pseudomonas aeruginosa is an opportunistic pathogen associated with nosocomial infections and disease complications. In the lungs of cystic fibrosis (CF) individuals, biofilm growth plays a crucial role in the persistence and antibiotic resistance of P. aeruginosa. Some strains, adapted to the CF lung microenvironment, show distinguishable phenotypes linked to biofilm production when compared to other strains. Using a novel image analysis quantification approach with crystal violet-stained biofilms, we compared the biofilm formation of four different P. aeruginosa isolates in 24-well plates: PAO1, the reference strain, LESB58 from CF patients' lungs and PPF-1 and Urg-7, two environmental isolates from dental unit waterlines. We also observed the formation of biofilm-like structures (BLSs) floating in the medium and investigated growth inhibition of the attached biofilm and BLS with Mg2+ or Zn2+. Urg-7 produced the most attached biofilms, but not the most BLSs. Attached biofilms had different responses to cations than BLSs did, but the effect of the cations was similar for all strains. These results demonstrate some diversity of biofilm formation in P. aeruginosa and indicate that chemical inhibition of attached biofilm formation for a specific strain or isolate cannot be predicative of a result on other P. aeruginosa strains or on BLSs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".