Phylogenetic characterization of Salmonella enterica from pig production and humans in Thailand and Laos border provinces
Bibliographic record
Abstract
Background and Aim: The genetic relationship among serotypes of Salmonella enterica from food animals, food of animal origin, and human is of interest as the data could provide an important clue for the source of human infection. This study aimed to determine the genetic relatedness of S. enterica from pig production and human in Thailand-Laos border provinces. Materials and Methods: A total of 195 S. enterica serotypes isolated from pig and pork (n=178) and human (n=17) including four serotypes (Typhimurium, Rissen, Derby, and Stanley) were randomly selected to examine their genetic relatedness using highly conserved sequence of three genes (fim A, man B, and mdh). Results: The results showed that 195 Salmonella isolates of four different serotypes were grouped into five different clusters, and members of the same Salmonella serotypes were found in the same cluster. Salmonella isolated from pig production and human in Thailand-Laos border provinces represented overlapping population and revealed a high degree of similarity, indicating close genetic relationship among the isolates. Conclusion: The results support that the determination of Salmonella serotyping combined with analysis of phylogenetic tree can be used track the clonal evolution and genetic diversity of Salmonella serotypes in different host species.
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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 | 0.000 | 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".