Antimicrobial and Genomic Characterization of Salmonella Nigeria from Pigs and Poultry in Ilorin, North-central, Nigeria
Bibliographic record
Abstract
INTRODUCTION: Non-typhoidal Salmonella are major foodborne pathogens causing serious challenges to public health and food safety worldwide. This study aimed to determine the resistance, virulence genes, sequence type, using multi-locus sequence typing, plasmids and Single Nucleotide Polymorphisms (SNPs) of Salmonella enterica subsp. enterica serovar Nigeria (S. Nigeria) from livestock in Ilorin, North central Nigeria. METHODOLOGY: A total of 1,500 samples from pig (feces; n = 600) and poultry (feces, postmortem samples; n = 900) were collected and analyzed between 2014 to 2017. Presumptive Salmonella isolates were characterized by Whole Genome Sequencing (WGS). RESULTS: We recovered nine S. Nigeria serovars. All the isolates harbored a single point mutation parC(T57S) in addition to qnrB19 and the tetA gene. Furthermore, two plasmids, Col(pHAD28) and IncQ1 predicted to encode qnrB19 and tetA genes, respectively, were detected in all the strains. All the isolates belonged to a single sequence type (ST) 4911, the SNP-based phylogeny showed all the isolates to be highly related, in addition two clinical isolates from the United Kingdom (UK) and Canada, collected outside of this study, also fell into this cluster. Twenty virulence genes were identified from Salmonella Pathogenicity Islands (SPI), chromosomal and fimbriae loci. CONCLUSIONS: This study highlights the roles of pig and poultry in the emergence and spread of S. Nigeria serovar in Nigeria, sub-Sahara Africa. It also highlighted the importance of WGS in clinical and epidemiological surveillance. There is the need for collaborative research studies to investigate the public health importance of Salmonella enterica serovar Nigeria.
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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 | 0.000 |
| 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) | 0.000 | 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 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".