<i>Escherichia coli</i>serogroup O2 and O28ac O-antigen gene cluster sequences and detection of pathogenic<i>E. coli</i>O2 and O28ac by PCR
Bibliographic record
Abstract
The O-antigen gene clusters of Escherichia coli serogroups O2 and O28ac were sequenced, and PCR assays were developed to identify strains belonging to these 2 serogroups. Sixteen and 8 open reading frames were mapped to these loci in E. coli O2:H4 U 9-41 and E. coli O28ac:H25 96-3286, respectively. The wzx (O-antigen flippase) and wzy (O-antigen polymerase) genes in the E. coli O2 and O28ac O-antigen gene clusters were selected as targets for PCR assays for their identification. PCR assays targeting the wzx and wzy genes were specific for these serogroups, with one exception. Escherichia coli serogroup O42 strains gave positive results with wzx and wzy PCR assays targeting E. coli O28ac, and antiserum raised against O42 cross-reacted with serogroup O28ac strains. The O-antigen gene cluster of a strain of E. coli serogroup O42 was sequenced, and there were only 3 nt differences between the O-antigen gene clusters of the O28ac and O42 strains. Multiplex PCR assays targeting the O2 wzx gene, the stx1, stx2, hly, eae, and saa genes, and the O28ac wzx, ial, ipaC, and ipaH genes were developed for detecting Shiga toxin-producing E. coli O2 strains and enteroinvasive E. coli O28ac strains, respectively. The O2 and O28ac wzx and wzy genes can be used as diagnostic markers in PCR assays for rapid identification of these serogroups as an alternative to serotyping, and the multiplex PCR assays targeting serogroup-specific genes in combination with virulence genes can be used to identify and to detect pathogenic serogroup O2 and O28ac strains.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".