Genomically Informed Custom Selective Enrichment of Shiga-toxigenic E. coli (STEC) outbreak strains in foods
Bibliographic record
Abstract
Foodborne bacterial outbreaks caused by Shiga-toxigenic E. coli (STEC) continue to place a burden on public health systems in developed countries.The STEC family of pathogens is biochemically diverse and current microbiological methods for detecting STEC may be encumbered by lack of a universal selective enrichment method, especially against high levels of background microbiota.A method has been previously described where genomic antimicrobial resistance (AMR) prediction tools are used to inform selection of a custom enrichment technique for recovery of a target STEC strain from ground beef.Here we build upon that concept and demonstrate the broader applicability of custom selective enrichment using recovery of five unique STEC strains from ground beef, bean sprouts and spinach.Drastically improved recovery of STEC strains from microbiologically diverse foods was shown for all 9 antibiotics examined in this study.The ability to accurately leverage AMR traits in specific pathogens for their recovery from high levels of background microbiota suggests this approach can be universally applicable in support of foodborne illness investigations.
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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 | 0.000 |
| 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 | 0.001 |
| 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".