Worldwide distribution of <i>Pseudomonas aeruginosa</i> clone C strains in the aquatic environment and cystic fibrosis patients
Bibliographic record
Abstract
Highly successful bacterial clones have the ability to effectively colonize environmental niches and patients. However, the factors which determine the complex interplay between the colonization of environmental niches and patients are mainly unknown. In this study we show that Pseudomonas aeruginosa clone C strains are distributed worldwide and highly prone to infect cystic fibrosis (CF) patients in Canada, England, France and Germany. In Hanover, Germany and Vancouver, Canada, clone C strains are highly prevalent in the CF patient community, although the mechanisms of acquisition may have been different. All clone C strains showed highly related macrorestriction fragment pattern of the whole genome as visualized by pulsed-field gel electrophoresis and harboured the 102 kbp plasmid pKLC102. Comparison of three prevalent P. aeruginosa clones with different distribution between the environment and patients revealed that neither enhanced biofilm formation nor antibiotic resistance was responsible for the spread of clone C. Clone M, which was highly prevalent in the clinical environment such as sanitary facilities, lacked motility, which could explain its relatively low prevalence in CF patients. Elucidation of the mechanisms which lead to the prevalence of clone C strain in patients and the environment requires the investigation of additional phenotypes.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".