Design and validation of oligonucleotide primers suitable for waterborne bacterial pathogen detection via real-time qPCR
Bibliographic record
Abstract
Fecal coliforms have been used as indicators to evaluate health risks associated with the microbiological quality of water for many years. Recent studies have challenged their ability to accurately predict bacterial numbers in the natural environment. DNA-based assays are proposed candidates to replace existing methods, but protocols suited for standardized direct-use have not yet been sufficiently developed. The objective of this study was to examine the feasibility of using real-time quantitative PCR (qPCR) to detect contamination from five waterborne bacterial pathogens in surface and treated drinking waters. Robust oligonucleotide primers were assembled to target virulence-associated genes. Primers were found to have high specificity and increased sensitivity for low pathogen loads of 10 cells/mL, as determined experimentally via qPCR. Detection of pathogenic cells directly from an environmental matrix has also been demonstrated using a filtration-extraction procedure. The developed protocols have shown their potential for use in conjunction with traditional indicator techniques.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".