Rapid Concentration and Molecular Enrichment Approach for Sensitive Detection of Escherichia coli and Shigella Species in Potable Water Samples
Bibliographic record
Abstract
In this work, we used a rapid, simple, and efficient concentration-and-recovery procedure combined with a DNA enrichment method (dubbed CRENAME [concentration and recovery of microbial particles, extraction of nucleic acids, and molecular enrichment]), that we coupled to an Escherichia coli/Shigella-specific real-time PCR (rtPCR) assay targeting the tuf gene, to sensitively detect E. coli/Shigella in water. This integrated method was compared to U.S. Environmental Protection Agency (EPA) culture-based Method 1604 on MI agar in terms of analytical specificity, ubiquity, detection limit, and rapidity. None of the 179 non-E. coli/Shigella strains tested was detected by both methods, with the exception of Escherichia fergusonii, which was detected by the CRENAME procedure combined with the E. coli/Shigella-specific rtPCR assay (CRENAME + E. coli rtPCR). DNA from all 90 E. coli/Shigella strains tested was amplified by the CRENAME + E. coli rtPCR, whereas the MI agar method had limited ubiquity and detected only 65 (72.2%) of the 90 strains tested. In less than 5 h, the CRENAME + E. coli rtPCR method detected 1.8 E. coli/Shigella CFU whereas the MI agar method detected 1.2 CFU/100 ml of water in 24 h (95% confidence). Consequently, the CRENAME method provides an easy and efficient approach to detect as little as one Gram-negative E. coli/Shigella cell present in a 100-ml potable water sample. Coupled with an E. coli/Shigella-specific rtPCR assay, the entire molecular procedure is comparable to U.S. EPA Method 1604 on MI agar in terms of analytical specificity and detection limit but provides significant advantages in terms of speed and ubiquity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".