Use of the <i>espZ</i> Gene Encoded in the Locus of Enterocyte Effacement for Molecular Typing of Shiga Toxin-Producing <i>Escherichia coli</i>
Bibliographic record
Abstract
Infections with Shiga toxin-producing Escherichia coli (STEC) result in frequent cases of sporadic and outbreak-associated enteric bacterial disease in humans. Classification of STEC is by stx genotype (encoding the Shiga toxins), O and H antigen serotype, and seropathotype (subgroupings based upon the clinical relevance and virulence-related genotypes of individual serotypes). The espZ gene is encoded in the locus of enterocyte effacement (LEE) pathogenicity island responsible for the attaching and effacing (A/E) lesions caused by various E. coli pathogens (but not limited to STEC), and this individual gene ( approximately 300 bp) has previously been identified as hypervariable among these A/E pathogens. Sequence analysis of the espZ locus encoded by additional STEC serotypes and strains (including O26:H11, O121:H19, O111:NM, O145:NM, O165:H25, O121:NM, O157:NM, O157:H7, and O5:NM) indicated that distinct sequence variants exist which correlate to subgroups among these serotypes. Allelic discrimination at the espZ locus was achieved using Light Upon eXtension real-time PCR and by liquid microsphere suspension arrays. The allele subtype of espZ did not correlate with STEC seropathotype classification; however, a correlation with the allele type of the LEE-encoded intimin (eae) gene was supported, and these sequence variations were conserved among individual serotypes. The study focused on the characterization of three clinically significant seropathotypes of LEE-positive STEC, and we have used the observed genetic variation at a pathogen-specific locus for detection and subtyping of STEC.
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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) | 0.000 | 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 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".