Molecular and Antimicrobial Susceptibility Analyses Distinguish Clinical from Bovine Escherichia coli O157 Strains
Bibliographic record
Abstract
A population-based study combining (i) antimicrobial, (ii) genetic, and (iii) virulence analyses with molecular evolutionary analyses revealed segregative characteristics distinguishing human clinical and bovine Escherichia coli O157 strains from western Canada. Human (n = 50) and bovine (n = 50) strains of E. coli O157 were collected from Saskatchewan and Manitoba in 2006 and were analyzed by using the six-marker lineage-specific polymorphism assay (LSPA6), antimicrobial susceptibility analysis, the colicin assay, plasmid and virulence profiling including the eae, ehxA, espA, iha, stx1, stx2, stx2c, stx2d, stx2d-activatable, stx2e, and stx2f virulence-associated genes, and structure analyses. Multivariate logistic regression and Fisher's exact test strongly suggested that antimicrobial susceptibility was the most distinctive characteristic (P = 0.00487) associated with human strains. Among all genetic, virulence, and antimicrobial determinants, resistance to tetracycline (P < 0.000) and to sulfisoxazole (P < 0.009) were the most strongly associated segregative characteristics of bovine E. coli O157 strains. Among 11 virulence-associated genes, stx2c showed the strongest association with E. coli O157 strains of bovine origin. LSPA6 genotyping showed the dominance of the lineage I genotype among clinical (90%) and bovine (70%) strains, indicating the importance of lineage I in O157 epidemiology and ecology. Population structure analysis revealed that the more-diverse bovine strains came from a unique group of strains characterized by a high degree of antimicrobial resistance and high frequencies of lineage II genotypes and stx2c variants. These findings imply that antimicrobial resistance generated among bovine strains of E. coli O157 has a large impact on the population of this human pathogen.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".