Detection and quantification of<i>Legionella pneumophila</i>in water samples using competitive PCR
Bibliographic record
Abstract
The presence of high levels of Legionella pneumophila in man-made aquatic systems correlates with the incidence of nosocomial Legionnaires' disease. This requires a rapid, reliable, and sensitive quantification of L. pneumophila concentrations in suspected water systems. In this research, a homologous competitor was developed and evaluated in a L. pneumophila competitive polymerase chain reaction (cPCR) to quantify this human pathogen in a quick, cost-effective, and reliable way. Accuracy of cPCR was evaluated by analyzing cooling tower and tap water samples spiked with known concentrations of L. pneumophila bacteria, in parallel with the standard culture method. Legionella pneumophila amounts detected and calculated from cPCR and culture correlated very well: r = 0.998, P = 0.002 for tap water and r = 0.990, P = 0.009 for cooling tower water. Nevertheless, for both kinds of water samples, mean numbers of L. pneumophila calculated from cPCR results were always higher than those obtained by culture. This study makes it clear that the rapid, sensitive, and cost-effective L. pneumophila cPCR is a promising alternative to the standard time-consuming culture method and expensive real-time PCR to enumerate L. pneumophila bacteria in environmental water samples.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".