Food animal reservoir for extraintestinal pathogenic «Escherichia coli» causing human infections
Bibliographic record
Abstract
Studies of extraintestinal infections caused by genetically related strains of Escherichia coli among unrelated people have demonstrated the epidemic potential of this group of bacteria. These related extraintestinal pathogenic E. coli (ExPEC) may have a common source. Our group recently described how retail meat, particularly chicken, may be a reservoir for ExPEC causing human urinary tract infections (UTIs). By moving upstream on the farm to fork continuum, this study tests whether the reservoir for ExPEC is in food animals themselves. A total of 824 geographically and temporally matched E. coli isolates from cecal contents of slaughtered food animals (n=349) and human UTI (n=475) sources were compared. Using 6 different typing methods, an evolutionary relationship was observed between E. coli isolates from the food animal reservoir and human UTI. Moreover, chicken was the predominant animal species from where the related isolates originated. Using an evolutionary model, chicken was determined to be the most likely source of the human UTI isolates. This study confirmed that an animal reservoir, principally in chicken, may exist for ExPEC causing community-acquired UTI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".