A Comprehensive Review on Shiga Toxin Subtypes and Their Niche-Related Distribution Characteristics in Shiga-Toxin-Producing E. coli and Other Bacterial Hosts
Bibliographic record
Abstract
Shiga toxin (Stx), the main virulence factor of Shiga-toxin-producing E. coli (STEC), was first discovered in Shigella dysenteriae strains. While several other bacterial species have since been reported to produce Stx, STEC poses the most significant risk to human health due to its widespread prevalence across various animal hosts that have close contact with human populations. Based on its biochemical and molecular characteristics, Shiga toxin can be grouped into two types, Stx1 and Stx2, among which a variety of variants and subtypes have been identified in various bacteria and host species. Interestingly, the different Stx subtypes appear to vary in their host distribution characteristics and in the severity of diseases that they are associated with. As such, this review provides a comprehensive overview on the bacterial species that have been recorded to possess stx genes to date, with a specific focus on the various Stx subtype variants discovered in STEC, their prevalence in certain host species, and their disease-related characteristics. This review provides a better understanding of the Stx subtypes and highlights the need for rapid and accurate approaches to toxin subtyping for the proper evaluation of the health risks associated with Shiga-toxin-related bacterial food contamination and human infections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".