Influence of temperature and pH on induction of Shiga toxin Stx1a in Escherichia coli
Bibliographic record
Abstract
Shiga toxin-producing strains represent pathogenic group that is of concern in food production. The present study evaluated forty-eight E. coli isolates (11 with intact stx gene, while remaining isolates presented only stx-fragments) for Shiga toxin production. The four most expressive stx-producers (O26, O103, O145, and O157) were selected to evaluate effects of pH (3.5, 4.5, and 7) and temperature (35, 40, and 50°C). After determining acid stress effects in media on Stx-induction, we mimicked “in natura” conditions using milk, apple, and orange juices. Only isolates that showed the presence of intact stx gene (11/48) produced Shiga toxin. In addition, acid pH had a role in down-regulating the production of Shiga toxin, in both lactic acid and juices. In contrast, non-lethal heating (40°C), when in neutral pH and milk was a favorable environment to induce Shiga toxin. Lastly, two isolates (O26 and O103) showed a higher capacity to produce Shiga toxin and were included in a genomic cluster with other E. coli involved in worldwide foodborne outbreaks. The induction of this toxin when subjected to 40°C may represent a potential risk to the consumer, since the pathogenic effect of oral ingestion of Shiga toxin has already been proved in an animal model.
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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 | 0.000 |
| 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 | 0.001 | 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".