Virulence properties and antimicrobial susceptibility of Shiga toxin-producing Escherichia coli strains isolated from healthy cattle from Paraná State, Brazil
Bibliographic record
Abstract
The presence of Shiga toxin-producing Escherichia coli (STEC) strains in feces samples of cattle was determined using the cytotoxicity assay on Vero cells and a screening PCR system to detect stx genes. The STEC isolates were serotyped, tested for antimicrobial susceptibility, and analyzed for virulence genes using multiplex PCR. The verocytotoxin-producing E. coli - reverse passive latex agglutination (VTEC-RPLA) assay was also used to detect Shiga toxin production. The frequency of cattle shedding STEC was 36%. The isolates belonged to 33 different serotypes, of which O10:H42, O98:H41, and O159:H21 had not previously been associated with STEC. The most frequent serotypes were ONT:H7 (10%), O22:H8 (7%), O22:H16 (7%), and ONT:H21 (7%). Most of the strains (96%) were susceptible to all antimicrobial agents tested. Shiga toxin was detected by the VTEC-RPLA assay in most (89%) of the STEC strains. The frequency of virulence markers was as follows: stx1, 10%; stx2, 43%; stx1 plus stx2, 47%; ehxA, 44%; eae, 1%; and saa, 38%. Several strains belong to serotypes associated with human disease, and most of them carried a stx2-type gene, suggesting that they represent a risk to human health. The screening PCR assay showed fewer false-negative results for STEC than the Vero-cell assay and is suitable for laboratory routine.
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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".