Bibliographic record
Abstract
The objective of this review is to highlight the importance of cattle in human disease due to Shiga toxin-producing Escherichia coli (STEC) and to discuss features of STEC that are important in human disease. Healthy dairy and beef cattle are a major reservoir of a diverse group of STEC that infects humans through contamination of food and water, as well as through direct contact. Infection of humans by STEC may result in combinations of watery diarrhea, bloody diarrhea, and hemolytic uremic syndrome. Systems of serotyping, subtyping, and virulence typing of STEC are used to aid in epidemiology, diagnosis, and pathogenesis studies. Severe disease and outbreaks of disease are most commonly due to serotype O157:H7, which, like most other highly pathogenic STEC, colonize the large intestine by means of a characteristic attaching and effacing lesion. This lesion is induced by a bacterial type III secretion system that injects effector proteins into the intestinal epithelial cell, resulting in profound changes in the architecture and metabolism of the host cell and intimate adherence of the bacteria. Severe disease in the form of bloody diarrhea and the hemolytic uremic syndrome is attributable to Shiga toxin (Stx), which exists as 2 major types, Stx1 and Stx2. The stx genes are encoded on temperate bacteriophages in the chromosome of the bacteria, and production and release of the toxin are highly dependent on induction of the phages. Regulation of the genes involved in induction of the attaching and effacing lesion, and production of Stx is complex. In addition to these genes that are clearly implicated in virulence, there are several putative virulence factors. A major public health goal is to prevent STEC-induced disease in humans. Studies aimed at understanding factors that affect carriage and shedding of STEC by cattle and factors that contribute to development of disease in humans are considered to be important in achieving this objective.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".