Incidence of Top 6 shiga toxigenic <em>Escherichia coli</em> within two Ontario beef processing facilities: Challenges in screening and confirmation testing
Bibliographic record
Abstract
The incidence of the Top 6 STEC serotypes was determined in two beef slaughter houses. In total, 328 samples were taken of hides, de-hided carcasses and the plant environment. Samples were enriched in Tryptic Soy Broth containing novobiocin then screened using RT-PCR GeneDiskÒ system that targeted stx, eae and wzx genes. It was found that 92.5% (172 of 186) of the hide samples. 72.5% (29 of 40) de-hided samples and 84.3% (86 of 102) of the environmental samples returned presumptive positive results. Serotypes O103, O45 and O121 were most commonly encountered although all the Top 6 serotypes were represented within individual samples. However, attempts to recover the Top 6 serotypes by culturing proved unsuccessful despite screening up to 20 colonies per CHROMAgar® plate of enriched sample. The reasons for the discrepancy between the RT-PCR and culture methods were found to be due to low levels of the target in enriched samples, presence of virulence factors in different cells and also the transient retention of stx. With regards the latter it was found that strains harboring a full set of virulence factors (eae, stx) were more common in grown cultures held post-incubation at 4 °C for 14 days. Moreover, no stx gene was recovered when isolates were sub-cultured on TSA but was present in the same strains grown on CHROMAgar®. In total 39 STEC isolates were recovered with the majority harboring stx1, stx2, eae and hylA. Only 3 of the isolates had stable complement of virulence factors and were identified as O172:H28, O76:H7 and O187:H52. Although no Top 6 STEC were isolated the presence of virulent strains on carcasses with the potential to cause Hemolytic Uremic Syndrome is of concern. The significance of those STEC that transiently harbor virulence factors is unclear although clearly impacts on diagnostic performance robustness when screening for the Top 6 non-O157 STEC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".