Biofilm formation by South African non-O157 Shiga toxigenic <i>Escherichia</i> <i>coli</i> on stainless steel coupons
Bibliographic record
Abstract
This study examined the biofilm-forming ability of six non-O157 Shiga-toxin-producing Escherichia coli (STEC) strains: O116:H21, wzx-Onovel5:H19, O129:H21, O129:H23, O26:H11, and O154:H10 on stainless steel coupons after 24, 48, and 72 h of incubation at 22 °C and after 168 h at 10 °C. The results of crystal violet staining revealed that strains O129:H23 and O154:H10 were able to form biofilms on both the submerged surface and the air–liquid interface of coupons, whereas strains O116:H21, wzx-Onovel5:H19, O129:H21, and O26:H11 formed biofilm only at the air–liquid interface. Viable cell counts and scanning electron microscopy showed that biofilm formation increased (p < 0.05) over time. The biofilm-forming ability of non-O157 STEC was strongest (p < 0.05) at 22 °C after 48 h of incubation. The strongest biofilm former regardless of temperature was O129:H23. Generally, at 10 °C, weak to no biofilm was observed for isolates O154:H10, O116:H21, wzx-Onovel5:H19, O26:H11, and O129:H21 after 168 h. This study found that temperature affected the biofilm-forming ability of non-O157 STEC strains. Overall, our data indicate a high potential for biofilm formation by the isolates at 22 °C, suggesting that non-O157 STEC strains could colonize stainless steel within food-processing facilities. This could serve as a potential source of adulteration and promote the dissemination of these potential pathogens in food.
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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".