Detection of viable but nonculturable <i>Escherichia coli</i> O157:H7 using propidium monoazide treatments and qPCR
Bibliographic record
Abstract
Escherichia coli O157:H7 can enter into a viable but nonculturable (VBNC) state under stress conditions. The aims of the present study were to examine the influences of environmental factors on the survivability and culturability of E. coli O157:H7 and to develop an approach for accurate detection of VBNC E. coli O157:H7. The E. coli O157:H7 strain ATCC 6589 was inoculated into 3 induction microcosm models: (i) Luria-Bertani broth, (ii) sterilized tap water, and (iii) sterilized physiological saline solution. Our results showed that low temperature and nutritional starvation significantly impacted on the survivability of E. coli O157:H7 cells and that the in-vitro-induced VBNC cells were capable of resuscitating under normal temperature and appropriate nutrients. We tested the effectiveness of an approach combining propidium monoazide (PMA) treatment with real-time polymerase chain reaction (PMA-qPCR) for accurate quantification of total, viable, dead, and VBNC cells under different induction microcosm models. Our results indicated different threshold cycle (Ct) values for PMA-treated cells and untreated cells (ΔCt = 4.97, 4.29, and 3.30 for Luria-Bertani broth, sterilized tap water, and sterilized physiological saline solution, respectively). We determined the quantification limit of this PMA-qPCR approach to be 1 × 10(2) cells·mL(-1), providing sufficient sensitivity for detection of VBNC E. coli O157:H7 cells to no less than 100 cells·mL(-1). This study clearly demonstrated the feasibility and effectiveness of using PMA-qPCR to accurately quantify E. coli O157:H7 in a VBNC state.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".