Adaptation of a real-time PCR method for the detection and quantification of pathogenic leptospires in environmental water
Bibliographic record
Abstract
Leptospirosis is a major zoonotic disease that affects humans and animals in all continents, in both rural and urban areas. In Europe, metropolitan France is the most affected country, with about 300 human cases declared per year. In France, although leptospirosis is now mostly considered as a recreational disease related to freshwater areas, isolation of pathogenic leptospires from environmental water samples still remains difficult. It thus seemed important to set up an efficient method to detect and quantify these bacteria in this environment. We determined a DNA extraction method suitable for freshwater samples and adapted a real-time quantitative PCR based on the detection of the LipL32 gene using the SYBR green chemistry. The method developed is specific for pathogenic Leptospira. It permits the detection of all the pathogenic strains tested and none of the saprophytic strains. Quantification is possible between 10 and 10(7) bacteria/mL, and therefore, the method represents a tool that could be integrated into future public health surveillance programs for recreational freshwater areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".