Genomic analysis of the MLST population structure and antimicrobial resistance genes associated with <i>Salmonella enterica</i> in Mexico
Bibliographic record
Abstract
Salmonella enterica is one of the most commonly reported foodborne pathogens by public health agencies worldwide. In this study, the multilocus sequence typing (MLST) population structure and frequency of antimicrobial resistance (AMR) genes were evaluated in S. enterica strains from Mexico ( n = 2561). The most common sources of isolation were food (44.28%), environment (27.41%), animal-related (24.83%), and human (3.48%). The most prevalent serovars were Newport (8.51%), Oranienburg (7.03%), Anatum (5.78%), Typhimurium (5.12%), and Infantis (4.57%). As determined by the 7-gene MLST scheme, the most frequent sequence types were ST23, ST64, and ST32. The core genome MLST scheme identified 132 HC2000 and 195 HC900 hierarchical clusters, with the HC2000_2 cluster being the most prevalent in Mexico ( n = 256). A total of 78 different AMR genes belonging to 13 antimicrobial classes were detected in 638 genomic assemblies of S. enterica. The most frequent class was aminoglycosides (31.76%), followed by tetracyclines (12.53%) and sulfonamides (11.91%). These results can help public health agencies in Mexico prioritize their efforts and resources to increase the genomic sequencing of circulating Salmonella strains. Additionally, they provide valuable information for local and global public health efforts to reduce the impact of foodborne diseases and AMR.
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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".