Molecular serotyping and virulence potential of Listeria monocytogenes isolated from bovine, swine and human in the province of Quebec
Bibliographic record
Abstract
Listeria monocytogenes (L mono) cause rare but critical diseases, particularly for at risk population that include pregnant women. Food-borne origin of listeriosis is clearly recognised only since 1984. Since then, a great number of grouped cases occurred and milk or meat products, particularly pork meat, were implicated. Management of this zoonotic pathogen considers all strains as at equal risk. Recently a new perspective for characterisation of strain virulence was allowed since unaltered sequence of InlA was recognised as a key for strain virulence. Such complete InlA were reported as infrequent in so called environmental strains. Analyses of inlA sequences in strains involved in clinical cases will contribute to establish a risk based surveillance of L mono in food production. The aim of the project was, based on serovar and InlA sequencing characterisations of the strains, to compare L mono involved in animal from human cases, and clinical strains from environmental ones. In Quebec in 2013/2014 the surveillance of L mono clinical isolates provided a total of 20 strains from animal origin, and 16 PFGE-type isolated from human cases. The strain collection was completed by 32 L mono strains from holding pens of 3 main pork slaughter facilities in Quebec in 2011/2014. A PCR multiplex PCR protocol for serogrouping was used, and we propose a complement to easily reach the serovar identification (flaA PCR and agglutination against limited number of serum). inlA gene sequencing allows analysing the presence of SNP that conduct to truncated or modified InlA (PMSC-SNP). Serovar analyses show that proportions of IVB IIB vs. IIA serogroups differ according to the origin of the strain (Fisher p<0.05). Detection of low proportion of PMSC-SNP in inlA gene from clinical origin will be discussed in perspective of industrial management of the L mono risk.
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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 | 0.001 |
| 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) | 0.001 | 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".