Aflatoxin, Fumonisin and Shiga Toxin-Producing Escherichia coli Infections in Calves and the Effectiveness of Celmanax®/Dairyman’s Choice™ Applications to Eliminate Morbidity and Mortality Losses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mycotoxin mixtures are associated with Shiga toxin-producing Escherichia coli (STEC) infections in mature cattle. STEC are considered commensal bacteria in mature cattle suggesting that mycotoxins provide a mechanism that converts this bacterium to an opportunistic pathogen. In this study, we assessed the mycotoxin content of hemorrhaged mucosa in dairy calves during natural disease outbreaks, compared the virulence genes of the STECs, evaluated the effect of the mucosal mycotoxins on STEC toxin expression and evaluated a Celmanax®/Dairyman's Choice™ application to alleviate disease. As for human infections, the OI-122 encoded nleB gene was common to STEC genotypes eliciting serious disease. Low levels of aflatoxin (1-3 ppb) and fumonisin (50-350 ppb) were detected in the hemorrhaged mucosa. Growth of the STECs with the mycotoxins altered the secreted protein concentration with a corresponding increase in cytotoxicity. Changes in intracellular calcium indicated that the mycotoxins increased enterotoxin and pore-forming toxin activity. A prebiotic/probiotic application eliminated the morbidity and mortality losses associated with the STEC infections. Our study demonstrates: the same STEC disease complex exists for immature and mature cattle; the significance of the OI-122 pathogenicity island to virulence; the significance of mycotoxins to STEC toxin activity; and, finally, provides further evidence that prebiotic/probiotic applications alleviate STEC shedding and mycotoxin/STEC interactions that lead to disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it