Characterization of Monkey Enteropathogenic <i>Escherichia coli</i> (EPEC) and Human Typical and Atypical EPEC Serotype Isolates from Neotropical Nonhuman Primates
Bibliographic record
Abstract
Enteropathogenic Escherichia coli (EPEC) has been associated with infantile diarrhea and mortality in humans in developing countries. While diarrhea is also a major problem among primates kept in captivity, the role of E. coli is unclear. This study was designed to characterize diarrheagenic E. coli recovered from the feces of 56 New World nonhuman primates, primarily marmosets (Callithrix spp.). Seventeen of the 56 primates had signs of diarrhea and/or enteritis. E. coli recovered from feces from these animals was tested by PCR for genes encoding virulence factors of diarrheagenic E. coli and for patterns of adherence to HeLa cells. In addition, isolates were characterized by the fluorescence actin staining test and by their ability to induce attaching and effacing lesions. PCR for the eae gene was positive in 10 of the 39 (27%) apparently healthy animals and in 8 of the 17 (47%) animals with diarrhea and/or enteritis. Colonies of eae(+) E. coli were serotyped and examined by PCR for genes encoding EPEC virulence markers. The eae(+) E. coli isolates recovered from both healthy and sick nonhuman primates demonstrated virulence-associated attributes similar to those of EPEC strains implicated in human disease and are designated monkey EPEC. The results presented here indicate that EPEC may be a significant pathogen for nonhuman primates, deserving further investigation. The similarities between the affected animals investigated in this study and human EPEC infections suggest that marmosets may represent an important model for EPEC in humans.
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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) | 0.000 | 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) | 0.001 | 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".