Enteric Escherichia coli O157:H7 in Cattle, and the Use of Mice as a Model to Elucidate Key Aspects of the Host-Pathogen-Microbiota Interaction: A Review
Bibliographic record
Abstract
Enterohemorrhagic Escherichia coli (EHEC) serotype O157:H7 is responsible for foodborne disease outbreaks, typically associated with the consumption of undercooked foods contaminated with cattle manure containing the bacterium. At present, effective mitigations do not exist. Many of the factors regulating enteric colonization by E. coli O157:H7 in cattle, and how cattle respond to the bacterium are unknown. In this regard, intestinal colonization locations, shedding patterns, interactions with the enteric microbiota, and host immune responses to infection are current knowledge gaps. As disturbances to host homeostasis are believed to play an important role in the enteric survival of the bacterium, it is important to consider the potential importance of stress during cattle production. Husbandry logistics, cost, and the high genetic, physiological, and microbial heterogeneity in cattle has greatly hampered the ability of researchers to elucidate key aspects of the host-pathogen-microbiota interaction. Although mice have not been extensively used as a cattle model, the utilization of murine models has the potential to identify mechanisms to facilitate hypothesis formulation and efficacy testing in cattle. Murine models have been effectively used to mechanistically examine colonization of the intestine, host responses to infection, and to interactively ascertain how host physiological status (e.g., due to physiological stress) and the enteric microbiota influences colonization and disease. In addition to reviewing the relevant literature on intestinal colonization and pathogenesis, including existing knowledge gaps, the review provides information on how murine models can be used to elucidate mechanisms toward the development of rationale-based mitigations for E. coli O157:H7 in cattle.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".