Prevalence of Diarrhea-Associated Virulence Genes and Genetic Diversity in Escherichia coli Isolates from Fecal Material of Various Animal Hosts
Bibliographic record
Abstract
In order to assess the health risk associated with a given source of fecal contamination using bacterial source tracking (BST), it is important to know the occurrence of potential pathogens as a function of host. Escherichia coli isolates (n=593) from the feces of diverse animals were screened for various virulence genes: stx1 and stx2 (Shiga toxin-producing E. coli [STEC]), eae and EAF (enteropathogenic E. coli [EPEC]), STh, STp, and LT (enterotoxigenic E. coli [ETEC]), and ipaH (enteroinvasive E. coli [EIEC]). Eleven hosts were positive for only the eae (10.11%) gene, representing atypical EPEC, while two hosts were positive for both eae and EAF (1.3%), representing typical EPEC. stx1, stx2, or both stx1 and stx2 were present in 1 (0.1%,) 10 (5.56%), and 2 (1.51%) hosts, respectively, and confirmed as non-O157 by using a E. coli O157 rfb (rfbO157) TaqMan assay. STh and STp were carried by 2 hosts (2.33%) and 1 host (0.33%), respectively, while none of the hosts were positive for LT and ipaH. The repetitive element palindromic PCR (rep-PCR) fingerprint analysis identified 221 unique fingerprints with a Shannon diversity index of 2.67. Multivariate analysis of variance revealed that majority of the isolates clustered according to the year of sampling. The higher prevalence of atypical EPEC and non-O157 STEC observed in different animal hosts indicates that they can be a reservoir of these pathogens with the potential to contaminate surface water and impact human health. Therefore, we suggest that E. coli from these sources must be included while constructing known source fingerprint libraries for tracking purposes. However, the observed genetic diversity and temporal variation need to be considered since these factors can influence the accuracy of BST results.
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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 | 0.001 | 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".