Subtyping of a Large Collection of Historical Listeria monocytogenes Strains from Ontario, Canada, by an Improved Multilocus Variable-Number Tandem-Repeat Analysis (MLVA)
Bibliographic record
Abstract
Listeria monocytogenes is responsible for severe and often fatal food-borne infections in humans. A collection of 2,421 L. monocytogenes isolates originating from Ontario's food chain between 1993 and 2010, along with Ontario clinical isolates collected from 2004 to 2010, was characterized using an improved multilocus variable-number tandem-repeat analysis (MLVA). The MLVA method was established based on eight primer pairs targeting seven variable-number tandem-repeat (VNTR) loci in two 4-plex fluorescent PCRs. Diversity indices and amplification rates of the individual VNTR loci ranged from 0.38 to 0.92 and from 0.64 to 0.99, respectively. MLVA types and pulsed-field gel electrophoresis (PFGE) patterns were compared using Comparative Partitions analysis involving 336 clinical and 99 food and environmental isolates. The analysis yielded Simpson's diversity index values of 0.998 and 0.992 for MLVA and PFGE, respectively, and adjusted Wallace coefficients of 0.318 when MLVA was used as a primary subtyping method and 0.088 when PFGE was a primary typing method. Statistical data analysis using BioNumerics allowed for identification of at least 8 predominant and persistent L. monocytogenes MLVA types in Ontario's food chain. The MLVA method correctly clustered epidemiologically related outbreak strains and separated unrelated strains in a subset analysis. An MLVA database was established for the 2,421 L. monocytogenes isolates, which allows for comparison of data among historical and new isolates of different sources. The subtyping method coupled with the MLVA database will help in effective monitoring/prevention approaches to identify environmental contamination by pathogenic strains of L. monocytogenes and investigation of outbreaks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".