Induction of Viable but Nonculturable Escherichia coli O157:H7 in the Phyllosphere of Lettuce: a Food Safety Risk Factor
Bibliographic record
Abstract
Escherichia coli O157:H7 continues to be an important human pathogen and has been increasingly linked to food-borne illness associated with fresh produce, particularly leafy greens. The aim of this work was to investigate the fate of E. coli O157:H7 on the phyllosphere of lettuce under low temperature and to evaluate the potential hazard of viable but nonculturable (VBNC) cells induced under such stressful conditions. First, we studied the survival of six bacterial strains following prolonged storage in water at low temperature (4°C) and selected two strains with different nonculturable responses for the construction of E. coli O157:H7 Tn7gfp transformants in order to quantitatively assess the occurrence of human pathogens on the plant surface. Under a suboptimal growth temperature (16°C), both E. coli O157:H7 strains maintained culturability on lettuce leaves, but under more stressful conditions (8°C), the bacterial populations evolved toward the VBNC state. The strain-dependent nonculturable response was more evident in the experiments with different inoculum doses (10(9) and 10(6) E. coli O157:H7 bacteria per g of leaf) when strain BRMSID 188 lost culturability after 15 days and strain ATCC 43895 lost culturability within 7 days, regardless of the inoculum dose. However, the number of cells entering the VBNC state in high-cell-density inoculum (approximately 55%) was lower than in low-cell-density inoculum (approximately 70%). We recorded the presence of verotoxin for 3 days in samples that contained a VBNC population of 4 to 5 log(10) cells but did not detect culturable cells. These findings indicate that E. coli O157:H7 VBNC cells are induced on lettuce plants, and this may have implications regarding food safety.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".