Competition during enrichment of pathogenic <i>Escherichia coli</i> may result in culture bias
Bibliographic record
Abstract
Deadly outbreaks and illnesses due to Shiga toxin-producing Escherichia coli (STEC) occur worldwide; however, the cultivation methods required for adequate monitoring and traceback investigations are inefficient at best. Detection of STEC relies heavily on enrichment; yet no standard media or protocols exist. Furthermore, whether enrichment may bias detection of multiple STEC serogroups from complex samples is unknown. Thus, 14 STEC strains of serogroups O157 and the top six non-O157s (O26, O45, O103, O111, O121, and O145) were enriched in pairs for 6–78 h in broth and evaluated by quantitative polymerase chain reaction (qPCR). Here we show that a conventional 6-h enrichment protocol did not result in intra-species culture bias for the isolates tested. However, subsequent enrichments often produced biased cultures, with differences in the qPCR gene copy number ≥2 log 10 apparent in 12%, 38%, and 52% of competitions after 30, 54, and 78 h of consecutive enrichments, respectively. Some strains were able to prevail and (or) out-compete the opponent strain in 100% of competitions. Our results suggest that culture bias should be considered and (or) evaluated further due to the potential implications during routine pathogen screening and outbreak investigations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".