Detection methods for carbapenem-resistant Pseudomonas aeruginosa in surface water and wastewater
Bibliographic record
Abstract
Water systems can act as an important reservoir for Pseudomonas aeruginosa, which can pose public health risks during human contact. Carbapenem resistance is one of the most concerning resistances in P. aeruginosa making it a high-priority pathogen according to the World Health Organization (WHO), due to its ability to cause difficult-to-treat infections. Culture methods for detecting carbapenem-resistant P. aeruginosa (CRPA) have generally been developed for clinical samples but rarely for water. Therefore, the aim of this study was to develop a standardized and widely applicable method for detecting CRPA in water samples, both in surface water and wastewater. Growth of well-characterized strains of CRPA was determined in four different selective agar media which were each supplemented with imipenem, meropenem, or ceftazidime, including after an enrichment. Wastewater samples presumptively containing CRPA (naturally contaminated samples) and surface water samples spiked with well-characterized CRPA strains were filtered and cultured using selective agar plates supplemented with imipenem and meropenem, with and without a prior enrichment step. M-PA-C agar plates supplemented with imipenem performed best compared to other selective agar plates with respect to growth of well-characterized CRPA strains and suppression of background growth of other bacteria and carbapenem-susceptible P. aeruginosa (CSPA) with positive predictive value of 80-100 %. A prior step using asparagine proline broth with vancomycin is recommended for higher specificity considering direct plating was unsuccessful due to extensive background growth. A combination of nonselective enrichment with selective plating (i.e., addition of imipenem to the plate) is recommended to achieve low limits of detection. The addition of imipenem in the broth instead of in solid agar plates eliminated some of the CRPA strains, producing an increase in the lower limit of detection and a lower PPV (32 % vs 73 %).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".