Quantitative surveillance of shiga toxins 1 and 2, Escherichia coli O178 and O157 in feces of western-Canadian slaughter cattle enumerated by droplet digital PCR with a focus on seasonality and slaughterhouse location
Bibliographic record
Abstract
Often Escherichia coli are harmless and/or beneficial bacteria inhabiting the gastrointestinal tract of livestock and humans. However, Shiga toxin-producing E. coli (STEC) have been linked to human disease. Cattle are the primary reservoir for STEC and STEC "super-shedders" are considered to be a major contributor in animal to animal transmission. Among STEC, O157:H7 is the most recognized serotype, but in recent years, non-O157 STEC have been increasingly linked to human disease. In Argentina and Germany, O178 is considered an emerging pathogen. Our objective was to compare populations of E. coli O178, O157, shiga toxin 1 and 2 in western Canadian cattle feces from a sampling pool of ~80,000 beef cattle collected at two slaughterhouses. Conventional PCR was utilized to screen 1,773 samples for presence/absence of E. coli O178. A subset of samples (n = 168) was enumerated using droplet digital PCR (ddPCR) and proportions of O178, O157 and shiga toxins 1 & 2 specific-fragments were calculated as a proportion of generic E. coli (GEC) specific-fragments. Distribution of stx1 and stx2 was determined by comparing stx1, stx2 and O157 enumerations. Conventional PCR detected the presence of O178 in 873 of 1,773 samples and ddPCR found the average proportion of O178, O157, stx1 and stx2 in the samples 2.8%, 0.6%, 1.4% and 0.5%, respectively. Quantification of stx1 and stx2 revealed more virulence genes than could be exclusively attributed to O157. Our results confirmed the presence of E. coli O178 in western Canadian cattle and ddPCR revealed O178 as a greater proportion of GEC than was O157. Our results suggests: I) O178 may be an emerging subgroup in Canada and II) monitoring virulence genes may be a more relevant target for food-safety STEC surveillance compared to current serogroup screening.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".