Development of a New Molecular Subtyping Tool for Salmonella enterica Serovar Enteritidis Based on Single Nucleotide Polymorphism Genotyping Using PCR
Bibliographic record
Abstract
The lack of a sufficiently discriminatory molecular subtyping tool for Salmonella enterica serovar Enteritidis has hindered source attribution efforts and impeded regulatory actions required to disrupt its food-borne transmission. The underlying biological reason for the ineffectiveness of current molecular subtyping tools such as pulsed-field gel electrophoresis (PFGE) and phage typing appears to be related to the high degree of clonality of S. Enteritidis. By interrogating the organism's genome, we previously identified single nucleotide polymorphisms (SNP) distributed throughout the chromosome and have designed a highly discriminatory PCR-based SNP typing test based on 60 polymorphic loci. The application of the SNP-PCR method to DNA samples from S. Enteritidis strains (n = 55) obtained from a variety of sources has led to the differentiation and clustering of the S. Enteritidis isolates into 12 clades made up of 2 to 9 isolates per clade. Significantly, the SNP-PCR assay was able to further differentiate predominant PFGE types (e.g., XAI.0003) and phage types (e.g., phage type 8) into smaller subsets. The SNP-PCR subtyping test proved to be an accurate, precise, and quantitative tool for evaluating the relationships among the S. Enteritidis isolates tested in this study and should prove useful for clustering related S. Enteritidis isolates involved in outbreaks.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".