Molecular Analysis as an Aid To Assess the Public Health Risk of Non-O157 Shiga Toxin-Producing <i>Escherichia coli</i> Strains
Bibliographic record
Abstract
Shiga toxin-producing Escherichia coli (STEC) strains are commensal bacteria in cattle with high potential for environmental and zoonotic transmission to humans. Although O157:H7 is the most common STEC serotype, there is growing concern over the emergence of more than 200 highly virulent non-O157 STEC serotypes that are globally distributed, several of which are associated with outbreaks and/or severe human illness such as hemolytic-uremic syndrome (HUS) and hemorrhagic colitis. At present, the underlying genetic basis of virulence potential in non-O157 STEC is unknown, although horizontal gene transfer and the acquisition of new pathogenicity islands are an expected origin. We used seropathotype classification as a framework to identify genetic elements that distinguish non-O157 STEC strains posing a serious risk to humans from STEC strains that are not associated with severe and epidemic disease. We report the identification of three genomic islands encoding non-LEE effector (nle) genes and 14 individual nle genes in non-O157 STEC strains that correlate independently with outbreak and HUS potential in humans. The implications for transmissible zoonotic spread and public health are discussed. These results and methods offer a molecular risk assessment strategy to rapidly recognize and respond to non-O157 STEC strains from environmental and animal sources that might pose serious public health risks to humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".