Investigating the dynamics of Salmonella contamination in integrated poultry companies using a whole genome sequencing approach
Bibliographic record
Abstract
The study of non-typhoid Salmonella in broiler integrations has been limited by the resolution of typing techniques. Although serotyping of Salmonella isolates is used as a traditional approach, it is not of enough resolution to clearly understand the dynamics of this pathogen within poultry companies. The aim of this research was to investigate the epidemiology and population dynamics of Salmonella serotypes in 2 poultry integrations using a whole genome sequencing approach. Two hundred and forty-three Salmonella isolates recovered from the broiler production chain of 2 integrated poultry companies were whole genome sequenced and analyzed with dedicated databases and bioinformatic software. The analyses of sequences revealed that S. Infantis was the most frequent serotype (82.3%). Most isolates showed a potential for resistance against medically important antibiotics and disinfectants. Furthermore, 97.5% of isolates harbored the pESI-like mega plasmid, that plays an important role in the global dissemination of AMR. SNP tree analysis showed that there were clones that are niche-specific while other ones were distributed throughout the broiler production chains. In this study, we demonstrated the potential of whole genome sequencing analysis for a comprehensive understanding of Salmonella distribution in integrated poultry companies. Data obtained with these techniques allow determination of the presence of genetic factors that play an important role in the environmental fitness and pathogenicity of Salmonella.
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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 | 0.000 | 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".