Biofilm Formation, Virulence Gene Profiles, and Antimicrobial Resistance of Nine Serogroups of Non-O157 Shiga Toxin–Producing <i>Escherichia coli</i>
Bibliographic record
Abstract
The objectives of this study were to characterize the phenotype and genotype of 36 non-O157 Shiga toxin-producing Escherichia coli (STEC) strains isolated from humans, ovines, or bovines, including the top 6 (O26, O45, O103, O111, O121, and O145) and three other serogroups implicated in serious illness (O91, O113, and O128). Biofilms were formed by all strains with intermediate to strong biofilm producers (n = 24) more common at 22°C than at 37°C (p < 0.001) and 48 and 72 h (p < 0.001) than 24 h of incubation time. Biofilm-forming potential differed by serogroup and origin with O113 and human strains exhibiting the highest potential (p < 0.001). Biofilm-associated genes, csgA/csgD/crl/fimH (100%), flu (94%), rpoS (92%), ehaA(α) (89%), and cah (72%), were most prevalent, while mlrA (22%) and ehaA(β) (14%) were least prevalent, although there was no clear compliment of genes associated with strains exhibiting the greatest biofilm-forming capacity. Among 12 virulence genes screened, iha and ehxA were present in 92% of the strains. The occurrence of stx1 in the top 6 serogroups (8/12, 67%) did not differ (p = 0.8) from other serogroups (17/24, 71%), but stx2 was less likely (confidence interval [CI] = 0.14-1.12; p = 0.04) to be in the former (9/24, 38%) than the latter (9/12, 75%). Excluding serogroups, O91 and O121, at least one strain per serogroup was resistant to between three and six antimicrobials. Streptomycin (31%), sulfisoxazole (31%), and tetracycline (25%) resistance was most common and was 35-50% less likely (p < 0.05) in human than animal strains. All non-O157 STEC strains were able to form biofilms on an abiotic surface, with some exhibiting resistance to multiple antimicrobials. Potential as a reservoir of antimicrobial resistance genes may be another hazard of biofilms in food-processing plants. As a result, future strategies to control these pathogens may include measures to prevent biofilms.
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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 | 0.001 |
| 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".