<i>Escherichia coli</i>from animal reservoirs as a potential source of human extraintestinal pathogenic<i>E. coli</i>
Bibliographic record
Abstract
Extraintestinal pathogenic Escherichia coli (ExPEC) are an important cause of urinary tract infections, neonatal meningitis and septicaemia in humans. Animals are recognized as a reservoir for human intestinal pathogenic E. coli, but whether animals are a source for human ExPEC is still a matter of debate. Pathologies caused by ExPEC are reported for many farm animals, especially for poultry, in which colibacillosis is responsible for huge losses within broiler chickens. Cases are also reported for companion animals. Commensal E. coli strains potentially carrying virulence factors involved in the development of human pathologies also colonize the intestinal tract of animals. This review focuses on the recent evidence of the zoonotic potential of ExPEC from animal origin and their potential direct or indirect transmission from animals to humans. As antimicrobials are commonly used for livestock production, infections due to antimicrobial-resistant ExPEC transferred from animals to humans could be even more difficult to treat. These findings, combined with the economic impact of ExPEC in the animal production industry, demonstrate the need for adapted measures to limit the prevalence of ExPEC in animal reservoirs while reducing the use of antimicrobials as much as possible.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".