Genetic diversity of listeria found in the meat processing environment
Bibliographic record
Abstract
Background. The safety of food production in relation to listeria is the key to the sanitary safety of the manufactured products. Molecular genetic methods for the analysis of listeria, including whole genome sequencing, are effective in monitoring persistent contaminants and in the epidemic investigation of foodborne infections. They have been adopted by the European Union, the USA and Canada. In Russia, multilocus and whole genome sequencing has proven itself in the analysis of clinical, food isolates, and listeria from the environment. The purpose of this work was to provide molecular genetic characterization of listeria found in meat processing plants. Material and methods. Microbiological methods, according to GOST 32031-2012, as well as multilocus sequencing, including the analysis of 7 housekeeping genes and 4 virulence genes, and genome-wide sequencing were used to characterize the isolated Listeria isolates. Results and discussion. Listeria monocytogenes accounted for 81% of washes sampled at two meat processing plants in Moscow, and 19% for L. welshimeri. The predominant genotype (Sequence Type, ST) of L. monocytogenes was ST 8. The variety was supplemented by ST321, ST121, ST2330 (CC9 — Clonal Complex 9). L. welshimeri, which prevailed in the second production, was represented by ST1050 and 2331. The genomic characteristics of L. welshimeri isolates confirmed their high adaptability both to production conditions (including resistance to disinfectants) and to metabolic characteristics of the gastrointestinal tract of animals. L. monocytogenes CC9 and CC121 and in other countries correlate with food production. However, L. monocytogenes CC8 and 321 can cause invasive listeriosis. The coincidence of the internalin profile of the production isolate ST8 with the clinical isolate ST8 and ST2096 (CC8) raises suspicion. Conclusions. The study showed the effectiveness of molecular genetic methods in determining the diversity of listeria found in meat processing plants and laid the foundations for monitoring persistent contaminants.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".