Pulsed-field gel electrophoresis typing, antibiotic resistance, and plasmid profiles of<i>Escherichia coli</i>strains isolated from foods
Bibliographic record
Abstract
Bacterial contamination in foods and antimicrobial resistance levels of common pathogenic strains causing food-borne disease are important in human health. Thus, typing technologies are important tools to determine primary sources of bacterial contamination. In this study, 40 Escherichia coli strains isolated from 85 food samples were evaluated in terms of genetic diversity, susceptibility to certain antibiotics, and plasmid profiles. Pulsed-field gel electrophoresis was used to identify the genetic relations of E. coli isolates. It was determined that the 40 E. coli strains revealed 32 different pulsotypes represented by 6 subtypes. Antibiotic susceptibility tests conducted by using a disc diffusion method against 15 antibiotics showed that although the isolates revealed 14 different types of resistance profiles, the strains showed the greatest resistance to ampicillin (77.5%), followed by ticarcillin-clavulanic acid (30%), tetracycline (22.5%), and cephalothin (14.5%). Plasmid isolations studies of the strains conducted by the method of alkaline lysis revealed that 18 (45%) of 40 E. coli strains contain 31 different plasmid bands ranging between 64.4 and 1 kb. The results showed that PFGE was a powerful method in tracking sources of food contamination and that the antibiotic resistance levels of food isolates were high and should be monitored.
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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 | 0.001 | 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) | 0.000 | 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".