Monitoring Seven Potentially Pathogenic <i>Escherichia coli</i> Serogroups in a Closed Herd of Beef Cattle from Weaning to Finishing Phases
Bibliographic record
Abstract
The goal of this study was to monitor Shiga toxin–producing Escherichia coli (STEC) serogroups and virulence genes in cattle (n = 30) originating from a closed herd. Fecal samples were collected (1) at weaning, (2) upon arrival to a feedlot, (3) after 30 days on feed (DOF), and (4) after 135 DOF. DNA was extracted from feces for detection of virulence and serogroup genes by polymerase chain reaction (PCR) and immunomagnetic separation and pulsed-field gel electrophoresis (PFGE) were performed to collect and subtype STEC isolates. The prevalence of each serogroup measured by PCR from weaning to 135 DOF was 23.3–80.0% for O26, 33.3–46.7% for O45, 70.0–73.3% for O103, 36.7–86.7% for O111, 56.7–6.7% for O121, 26.7–66.7% for O145, and 66.7–90.0% for O157. Total fecal samples positive for virulence genes were 87.5% for ehxA, 85.8% for stx1, 60.0% for stx2, 52.5% for eae, and 44.2% for the autoagglutinating adhesion gene, saa. The prevalence of each serogroup and virulence gene tended to increase by 135 DOF, with the exception of O121, stx2, and saa. The frequency of detection of some virulence genes was largely affected over time, most notably with saa and stx2 decreasing, and eae increasing when cattle were transitioned to concentrate-based diets. PFGE analysis of O157 and O103 fecal isolates revealed dominant pulsotypes, but the presence of identical O103 isolates, which differed in virulence profiles. Overall, this study showed that fecal shedding of E. coli serogroups and virulence-associated genes are highly variable over time as cattle move from ranch to feedlot. To mitigate STEC, it is important to understand the factors affecting both prevalence of individual serogroups and the presence of virulence factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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 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".