High-Throughput Genotyping of <i>Salmonella enterica</i> Serovar Typhi Allowing Geographical Assignment of Haplotypes and Pathotypes within an Urban District of Jakarta, Indonesia
Bibliographic record
Abstract
High-throughput epidemiological typing systems that provide phylogenetic and genotypic information are beneficial for tracking bacterial pathogens in the field. The incidence of Salmonella enterica serovar Typhi infection in Indonesia is high and is associated with atypical phenotypic traits such as expression of the j and the z66 flagellum antigens. Utilizing a high-throughput genotyping platform to investigate known nucleotide polymorphisms dispersed around the genome, we determined the haplotypes of 140 serovar Typhi isolates associated with Indonesia. We identified nine distinct serovar Typhi haplotypes circulating in Indonesia for more than 30 years, with eight of these present in a single Jakarta suburb within a 2-year period. One dominant haplotype, H59, is associated with j and z66 flagellum expression, representing a potential pathotype unique to Indonesia. Phylogenetic analysis suggests that H59 z66(+), j(+) isolates emerged relatively recently in terms of the origin of serovar Typhi and are geographically restricted. These data demonstrate the potential of high-throughput genotyping platforms for analyzing serovar Typhi populations in the field. The study also provides insight into the evolution of serovar Typhi and demonstrates the value of a molecular epidemiological technique that is exchangeable, that is internet friendly, and that has global utility.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".