Emerging novel sequence types of Staphylococcus aureus in Pakistan
Bibliographic record
Abstract
BACKGROUND: Despite an increasing incidence of Staphylococcus aureus infection and dissemination in Pakistan, the epidemiology of different Staphylococcus aureus research clones has been the subject of only a small number of investigations. By analyzing the collected data sequence, this study was designed to study the epidemiology of Staphylococcus aureus in the area using multilocus sequence typing (MLST). METHODS: A total of 1015 staphylococcus strains collected from the city's tertiary care facilities were biochemically screened, followed by antimicrobial susceptibility testing against a panel of 13 antibiotics. Analyzed methicillin-resistant Staphylococcus aureus (MRSA) was subjected to molecular characterization using multilocus sequence typing (MLST), clonal complex analysis, recombination testing, and phylogenetic analysis. RESULTS: Approximately 421 bacteria were verified as Staphylococcus aureus by biochemical analysis. 57% of the isolates exhibited multidrug resistance, of which 89% were found to be methicillin-resistant Staphylococcus aureus (MRSA). MLST results in a total of 39 sequence types (ST) and 5 clonal complexes (CC), out of which twenty-two STs were newly documented worldwide. The most common CC identified was CC8. The direct sequencing data also revealed significant shifts at MLST loci, with point mutations resulting in the aroE-343 and tpi-278 alleles. CONCLUSIONS: This study concludes that there is high diversity in the locally circulating clones of Staphylococcus aureus present in nature and that they are defined by their geographic epidemiology. These findings have practical implications for public health, including the need for tailored infection control strategies, antibiotic stewardship, global surveillance, and a deeper understanding of bacterial evolution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".