Development of a capture/enrichment sandwich ELISA for the rapid detection of enteropathogenic and enterohaemorrhagic Escherichia coli O26 strains
Bibliographic record
Abstract
AIMS: To improve the sensitivity of a monoclonal antibody (MAb 2F3) based enteropathogenic Escherichia coli (EPEC)/enterohaemorrhagic E. coli (EHEC) serogroup O26-specific sandwich ELISA (sELISA), using a capture/enrichment format of the assay. METHODS AND RESULTS: The sELISA utilized an EPEC/EHEC O26-specific MAb 2F3 as the capture reagent and an E. coli serogroup O26 lipopolysaccharide-specific polyclonal antibody in the development stage. Wells containing faeces test samples from bovine enteritis cases and agar colony sweep cultures from human diarrhoea cases, after a 2-h capture stage, were washed and enrichment of the captured cells was encouraged by addition of tryptone soya broth. After overnight incubation, the contents of each well were transferred to sterile wells and the sELISA completed. Any sELISA positive samples were then subcultured onto blood agar to recover and further characterize the positive cultures. The assay had a sensitivity of 10(3) CFU ml(-1). ELISA positive samples consisted of 21 (4.8%) of the 442 bovine and 19 (3.7%) of the 519 human samples tested, and ELISA positive EPEC/EHEC O26 strains were isolated from 11 and three of these samples respectively. CONCLUSION: The capture/enrichment method improved the sensitivity of a MAb-based sELISA for the detection of EPEC/EHEC O26 strains, and also contributed to an improved isolation rate of the organism from field samples. SIGNIFICANCE AND IMPACT OF THE STUDY: The application of a specific MAb in a capture/enrichment format of the sELISA, provides a prospectively suitable screening method for the detection of pathogenic bacteria from mixed culture samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".