Characterization of Shiga toxin – producing Escherichia coli infections in beef feeder calves and the effectiveness of a prebiotic in alleviating Shiga toxin - producing Escherichia coli infections
Bibliographic record
Abstract
BACKGROUND: In the less-sensitive mouse model, Shiga toxin-producing Escherichia coli (STEC) challenges result in shedding that reflect the amount of infection and the expression of virulence factors such as Shiga toxins (Stx). The purpose of this study was to characterize the contribution of STEC diversity and Stx expression to shedding in beef feeder calves and to evaluate the effectiveness of a prebiotic, Celmanax®, to alleviate STEC shedding. Fecal samples were collected from calves at entry and after 35 days in the feedlot in spring and summer. STECs were evaluated using selective media, biochemical profile, serotyping and Stx detection. Statistical analysis was performed using repeated measures ANOVA and logistic regression. RESULTS: At entry, non-O157 STEC were dominant in shedding calves. In spring, 21%, 14% and 14% of calves acquired O157, non-O157 and mixed STEC infections, respectively. In contrast, 45%, 48% and 46% of calves in summer acquired O157, non-O157 and mixed STEC infections, respectively. Treatment with a prebiotic, Celmanax®, in spring significantly reduced 50% of the O157 STEC infections, 50% of the non-O157 STEC infections and 36% of the STEC co-infections (P = 0.037). In summer, there was no significant effect of the prebiotic on STEC infections. The amount of shedding at entry was significantly related to the number and type of STECs present and Stx expression (r2 = 0.82). The same relationship was found for shedding at day 35 (r2 = 0.85), but it was also related to the number and type of STECs present at entry. Stx - producing STEC infections resulted in 100 to 1000 × higher shedding in calves compared with Stx-negative STECs. CONCLUSIONS: STEC infections in beef feeder calves reflect the number and type of STECs involved in the infection and STEC expression of Stx. Application of Celmanax® reduced O157 and non-O157 STEC shedding by calves but further research is required to determine appropriate dosages to manage STEC infections.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".