Molecular Characterization and Pathogenicity of Staphylococcus aureus Isolated from Benin-City, Nigeria
Bibliographic record
Abstract
While numerous studies examine the epidemiology and molecular characterization of Staphylococcus aureus in most developed countries, the detailed molecular characterization and molecular epidemiology of S. aureus strains and clones in Africa is lacking. We determined the molecular epidemiology and virulence of 81 non-duplicate isolates of S. aureus from Benin-City, Nigeria, collected during January–July 2016, and compared with global strains. Forty-seven isolates (58.0%) were found to be methicillin-sensitive Staphylococcus aureus (MSSA), while 34 (42.0%) were methicillin-resistant Staphylococcus aureus (MRSA). ST152-MSSA (24.7%) and ST7-MRSA-V (19.8%) were the dominant groups identified, which were not genetically related to global predominant strains, but rather exhibited regional dominance. An interesting finding of the study was the presence of highly related strains in the region, which differed primarily in their methicillin resistance gene carriage, staphylococcal cassette chromosome mec (SCCmec), with 99.4–99.7% relatedness between the genomes of the strains within the MRSA–MSSA pairs. This suggests that the strains within a pair are experiencing gain or loss of SCCmec within local conditions, with evolution continuing to diversify the strains to a small degree. This study represents the most comprehensive genetic and virulence study of S. aureus in 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".