Genotyping of Salmonella with lineage-specific genes: correlation with serotyping
Bibliographic record
Abstract
BACKGROUND: The bacterial genus Salmonella encompasses a large number of serotypes that are genetically very similar but biologically quite different, especially in pathogenic properties and host specificity. Serotyping has been used for the classification, identification, and epidemiological investigation due to its excellent discriminating power, but it cannot distinguish the different pathogenic lineages within a polyphyletic serotype. Additionally, very few institutions have the comprehensive set of antisera for typing. Therefore various studies have been performed to explore alternative assays to differentiate Salmonella isolates, such as the search for genes that can be used as potential molecular substitutes for serotyping. However, the genes tested so far have often given inconsistent results. METHODS: In this study, the discriminating power of seven genes to differentiate 309 Salmonella strains representing 26 serotypes was evaluated and the results were compared with those of other methods. RESULTS: The seven newly selected genes have a good power to differentiate different serovars. The tree based on the concatenated sequences of these genes revealed phylogenetic relationships of the bacteria consistent with that of the whole genome tree. CONCLUSION: Individual Salmonella lineages each have specific genes that can be used to differentiate Salmonella isolates on a phylogenetic basis.
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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 | 0.001 | 0.003 |
| 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".