Epidemiology, Virulence and Antimicrobial Resistance of Escherichia coli Isolated from Small Brazilian Farms Producers of Raw Milk Fresh Cheese
Bibliographic record
Abstract
This study aimed to identify contamination sources in raw milk and cheese on small farms in Brazil by isolating Escherichia coli at various stages of milk production and cheese manufacturing. The study targeted EAEC, EIEC, ETEC, EPEC, STEC, and ExPEC pathotypes, characterizing isolates for the presence of virulence genes, phylogroups, antimicrobial susceptibility, and phylogenetic relationships using PFGE and MLST. The presence of antimicrobial resistance genes and serogroups was also determined. Three categories of E. coli were identified: pathogenic, commensal, and ceftriaxone-resistant (ESBL) strains. Pathogenic EPEC, STEC, and ExPEC isolates were detected in milk and cheese samples. Most isolates belonged to phylogroups A and B1 and were resistant to antimicrobials such as nalidixic acid, ampicillin, kanamycin, streptomycin, sulfisoxazole, and tetracycline. Genetic analysis revealed that E. coli with identical virulence genes were present at different stages within the same farm. The most frequently identified serogroup was O18, and MLST identified ST131 associated with pathogenic isolates. The study concluded that E. coli was present at multiple points in milk collection and cheese production, with significant phylogroups and high antimicrobial resistance. These findings highlight the public health risk posed by contamination in raw milk and fresh cheese, emphasizing the need to adopt hygienic practices to control these microorganisms.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".