Influence of Season and Feedlot Location on Prevalence and Virulence Factors of Seven Serogroups of Escherichia coli in Feces of Western-Canadian Slaughter Cattle
Bibliographic record
Abstract
Pooled feces collected over two years from 1749 transport trailers hauling western-Canadian slaughter cattle were analysed by PCR for detection of Escherichia coli serogroups O26, O45, O103, O111, O121, O145, and O157. Sequential immunomagnetic separation was then used to collect bacterial isolates (n = 1035) from feces positive for target serogroups. Isolated bacteria were tested by PCR to confirm serogroup and the presence of eae, ehxA, stx1, and stx2 virulence genes. Based on PCR screening, serogroup prevalence in feces ranged from 7.0% (O145) to 94.4% (O103) with at least 3 serogroups present in 79.5% of samples. Origin of cattle affected serogroup PCR prevalence and O157 was most prevalent in feces from south-west Alberta (P < 0.001). All serogroups demonstrated seasonal variations in PCR prevalence, with O26, O45, O103, O121, and O157 least prevalent (P < 0.001) in cooler winter months, while uncommon serogroups O111 and O145 increased in prevalence during winter (P < 0.001). However, isolates collected during winter were predominantly from serogroups O103 and O45. No seasonal variation was noted in proportion of isolates which were Shiga toxin containing E. coli (STEC; P = 0.18) or positive for Shiga toxin and eae (enterohemorrhagic E. coli; EHEC; P = 0.29). Isolates of serogroups O111, O145, and O157 were more frequently EHEC than were others, although 37.6-54.3% of isolates from other serogroups were also EHEC. Shiga-toxin genes present also varied by geographic origin of cattle (P < 0.05) in all serogroups except O157. As cattle within feedlots are sourced from multiple regions, locational differences in serogroup prevalence and virulence genes imply existence of selection pressures for E. coli and their virulence in western-Canadian cattle. Factors which reduce carriage or expression of virulence genes, particularly in non-O157 serogroups, should be investigated.
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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 | 0.000 |
| 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".