Quantification of microcystin-producing cyanobacteria and E. coli in water by 5'-nuclease PCR
Bibliographic record
Abstract
AIMS: 5'-Nuclease (real-time, quantitative) PCR methodologies were developed and applied as diagnostic tools for the detection of microcystin-producing cyanobacteria and Escherichia coli in water. METHODS AND RESULTS: PCR was used to detect regions of the lacZ gene in E. coli, and the microcystin synthetase gene in microcystin-producing cyanobacteria. In environmental water samples, natural inhibitors to PCR were effectively removed with a prefiltration step and an EDTA wash. A lower detection limit of 10 cells ml(-1) was obtained with endpoint PCR detection. 5'-Nuclease PCR was used for microbial quantification of 1 ml inoculated water samples. We were able to detect down to three copies of our target genes per sample within about 2 h (post-DNA isolation) for both E. coli and microcystin-producing cyanobacteria. CONCLUSIONS: 5'-Nuclease PCR offers a rapid and sensitive method of bacterial quantification in water samples. SIGNIFICANCE AND IMPACT OF THE STUDY: 5'-Nuclease PCR can be adopted as an effective diagnostic tool for monitoring microbiological water quality, through coliform quantification, and detection of other waterborne microbial pathogens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".