Substituting Allose as the Primary Carbon Source During Enrichment Helps Improve Detection and Isolation of Lineage II Listeria monocytogenes From Food
Bibliographic record
Abstract
Testing of foods for low levels of the human pathogen, Listeria monocytogenes (Lm), involves a selective enrichment procedure. A nonpathogenic species of Listeria, L. innocua (Li), is often present in foods and food-manufacturing environments and is an interference organism for Lm detection due to competition during enrichment. The present study investigated whether a novel enrichment strategy incorporating the sugar allose into the secondary enrichment broth (allose method) could improve the detection of Lm from foods when Li is present. First, Canadian food isolates of Listeria spp. were tested to confirm recent reports that lineage II Lm (LII-Lm), but not Li, could metabolize allose. All LII-Lm isolates (n = 81), but not Li (n = 36), possessed the allose genes lmo0734-lmo0739, and could efficiently metabolize allose. Next, smoked salmon was contaminated with mixtures of LII-Lm and Li and tested using different enrichment procedures to compare the ability to recover Lm. Allose broth was more effective than Fraser Broth, with Lm detected in 87% (74 of 85) compared to 59% (50 of 85) of the samples (P < 0.05), following a common preenrichment. When evaluated against a current Health Canada method (MFLP-28), the allose method was more effective, with LII-Lm detected in 88% (57 of 65) compared to 69% (45 of 65) of the samples (P < 0.05). The allose method also remarkably increased the ratio of LII-Lm to Li postenrichment, which improved the ease of obtaining isolated Lm colonies for confirmation tests. Allose may therefore provide a tool for use when the presence of background flora interferes with Lm detection. As this tool is specifically applicable to a subset of Lm, the use of this method modification may provide a working example of tailoring methodology to target the known subtype of the pathogen of interest in an outbreak investigation, or for regular monitoring activities in conjunction with a PCR screen for allose genes on preenrichment cultures.
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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 | 0.000 |
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".