Molecular Surveillance of Shiga Toxigenic Escherichia coli in selected beef abattoirs in Osun State Nigeria
Bibliographic record
Abstract
Abstract Shiga toxigenic strains of E. coli (STEC) known to be etiological agents for diarrhea were screened for their incidence/ occurrence in selected abattoirs and retail meat sources in Osogbo metropolis of Osun State, Nigeria using a randomized block design. Samples were plated directly on selective and differential media and confirmed serologically using latex agglutination serotyping kit, then, multiplex PCR analysis was used to screen for the presence of specific virulence factors. The results showed a percent occurrence of STEC at the sampled sites ranging from 25.8–46.3%. None of the strains showed any visible agglutination with the O157 latex reagent. Sequence analysis of PCR products was performed on a representative isolate showing the highest combination of virulence genes. This sequence was subsequently submitted to GenBank with accession number MW463885. The sequence showed 100% coverage and 96.46% percentage identity with Escherichia coli O113:H21 (GenBank Accession number: CP031892.1) strain from Canada. From evolutionary analyses, the strain from Nigeria, sequenced in this study, is evolutionarily distant when compared with the publicly available sequences from Nigeria. Although no case of E. coli O157 was found within the study area, percent occurrence of non-O157 STEC as high as 46.3% at some of the sampled sites is worrisome and requires regulatory interventions in ensuring hygienic practices at the abattoirs within the study area.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".