Molecular Epidemiology of Salmonella enterica Serotype Dublin Isolated from 2011 to 2022 from Veal and Dairy Cattle in Pennsylvania
Bibliographic record
Abstract
The emergence of Salmonella enterica serotype Dublin (S. Dublin) presents significant challenges to animal and human health. We analyzed 109 S. Dublin isolates from bovine submissions to the Penn State Animal Diagnostic Laboratory between 2011 and 2022. Using whole genome sequencing, we assessed their phenotypic and genotypic resistance patterns and correlated these traits with case histories and pathology reports. Core-genome analysis identified cgSTs with similar allelic profiles between our isolates and those from the U.S. and Canada, while some cgSTs were unique to our study. Histopathologic findings suggest a predominance of respiratory and gastroenteric/hepatic lesions, aligning with the histopathological case definition for S. Dublin infection. Critically, all isolates were multidrug-resistant, particularly to ampicillin (87%), ceftiofur (89%), chlortetracycline (94%), oxytetracycline (94%), enrofloxacin (17%), florfenicol (94%), sulfadimethoxine (97%), and trimethoprim (20%). Plasmid genomic analysis unveiled distinct plasmid types including virulence, resistance, and hybrid plasmids, carrying unique compositions of virulence genes and antimicrobial resistance. These findings underscore the importance of managing calf movement to control the introduction and dissemination of new cgSTs in Pennsylvania and potentially nationwide. Furthermore, they emphasize the urgent need to mitigate S. Dublin transmission, combat antimicrobial resistance, and enhance surveillance efforts to effectively protect animal and human health.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".