Phenotypic stress response of Pseudomonas aeruginosa following culture in water microcosms
Bibliographic record
Abstract
The purpose of the present study was to explore the potential behavioural changes of Pseudomonas aeruginosa following growth in different aquatic environmental conditions. To achieve this, P. aeruginosa was cultured in various water microcosms for 12 months under fixed (pH, nutrients and temperature) factors. P. aeruginosa responses to these conditions were investigated using colony morphotype, biochemical and enzymatic characterisation, pyocin typing, serotyping, sensitivity to different classes of antibiotics and molecular identification. Results show that starvation in water microcosms lead to unusual phenotypes. Of interest is that the pyocin changed from 24/n in the wild type to 83/a following culture in the water microcosms, and the serotype changed from O6 in the wild type to O1 in microcosm-cultured P. aeruginosa. Furthermore, the starvation period in various aquatic microcosms enhanced the resistance of P. aeruginosa against beta-lactam antibiotics. Compared to the other aquatic environments, the seawater microcosm produced the greatest amount of variations in P. aeruginosa. Overall, data demonstrated a high adaptability of P. aeruginosa to environmental changes. This may explain the unusual antibiotic-resistant phenotypes belonging to P. aeruginosa species, and their capacity for spreading that leads to human 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".