Assessment of real-time PCR for quantification of Legionella spp. in spa water
Bibliographic record
Abstract
AIMS: Legionella bacteria ubiquitously colonize natural freshwater and are responsible for legionellosis in humans. Several cases of legionellosis have been associated in particular with the use of whirlpool spas. The objective of this study was to verify whether real-time PCR is applicable for the quantification of Legionella spp. in spa water. METHODS AND RESULTS: The study compared concentrations obtained by real-time PCR vs that obtained by conventional culture for 101 spa water samples. For the culture method, Legionella spp. were detected and quantified in 14 of 101 samples with measured concentrations ranging from 250 to 3.5 × 10(5) CFU l(-1). With the real-time PCR method, Legionella spp. were detected and quantified in 42 of 101 samples with concentrations ranging from 1000 to 6.1 × 10(7) GU l(-1). Results revealed a significant but weak correlation (r(2) = 0.1867) between the two methods. The positive predictive value (35%) of the PCR method compared to conventional culture herein was low. In contrast, the negative predictive value was excellent, reaching 93%. CONCLUSIONS: Real-time PCR could be used as a screening tool to rapidly ascertain the absence of Legionella spp. in spa water. However, a positive result involves the need to resort to conventional culture. SIGNIFICANCE AND IMPACT OF THE STUDY: Data of this study highlighted the pros and cons of quantification of Legionella spp. in spa water with real-time PCR using a commercial quantitative PCR kit in a routine laboratory, when compared to conventional culture.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".