Relative Cytotoxicity of <i>Escherichia coli</i> O157:H7 Isolates from Beef Cattle and Humans
Bibliographic record
Abstract
Differences and similarities between Escherichia coli O157:H7 isolates from beef cattle and those from sporadic human outbreaks are not fully elucidated. Here, we compared 44 O157:H7 isolates of bovine and human origins (22 isolates of each) to better understand their cytotoxic potential. The Shiga toxin genes stx1, stx2, or both were detected in the 44 isolates, and all elicited Vero cell cytotoxicity. The greatest cytotoxicity was caused by bovine isolates having only stx2 and which represented the majority of such isolates (81.8%). However, no correlation was found between the level of stx gene transcription and cytotoxicity. All human and bovine isolates possessed variant type stx2 and stx2c, respectively, as determined by PCR-restriction fragment length polymorphism. Isolates harboring both stx1 and stx2 genes were much more frequent in human isolates (86.4%). The combination stx1-stx2c found in only four bovine isolates was less cytotoxic. It is clear that cytotoxicity alone cannot account for the apparent inability of O157:H7 bovine isolates to cause diseases in humans. We have found that stx1-stx2-containing or stx1-stx2c-containing isolates were less cytotoxic than several bovine isolates having only stx2c, suggesting that the stx gene combination or other virulence genes in specific genetic lineages may affect the disease outcome.
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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".