Rapid Detection and Identification of<i>Yersinia pestis</i>from Food Using Immunomagnetic Separation and Pyrosequencing
Bibliographic record
Abstract
Interest has recently been renewed in the possible use of Y. pestis, the causative agent of plague, as a biological weapon by terrorists. The vulnerability of food to intentional contamination coupled with reports of humans having acquired plague through eating infected animals that were not adequately cooked or handling of meat from infected animals makes the possible use of Y. pestis in a foodborne bioterrorism attack a reality. Rapid, efficient food sample preparation and detection systems that will help overcome the problem associated with the complexity of the different matrices and also remove any ambiguity in results will enable rapid informed decisions to be made regarding contamination of food with biothreat agents. We have developed a rapid detection assay that combines the use of immunomagnetic separation and pyrosequencing in generating results for the unambiguous identification of Y. pestis from milk (0.9 CFU/mL), bagged salad (1.6 CFU/g), and processed meat (10 CFU/g). The low detection limits demonstrated in this assay provide a novel tool for the rapid detection and confirmation of Y. pestis in food without the need for enrichment. The combined use of the iCropTheBug system and pyrosequencing for efficient capture and detection of Y. pestis is novel and has potential applications in food biodefence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".