Molecular Characterization of Carbapenemase Production Among Gram-Negative Bacteria in Saudi Arabia
Bibliographic record
Abstract
We characterized the molecular basis of carbapenemase production in carbapenem-resistant Gram-negative bacteria isolated from hospitalized patients from Saudi Arabia in the year 2012. Isolates were collected from across the Kingdom and phenotypically tested for carbapenemase production. Polymerase chain reaction detection of carbapenemase genes was also performed. Our results indicate that in Saudi Arabia, OXA-48 and NDM-1 are the dominant carbapenemases among Enterobacteriaceae with low prevalence of VIM. The latter is the most prevalent metallo-beta-lactamase in Pseudomonas aeruginosa, whereas oxacillinases, OXA-23 in particular, are the dominant carbapenemases in Acinetobacter baumannii. No KPC or IMP genes were detected. Our study is the first report of OXA-48, NDM-1, and VIM-4 enzymes in Enterobacter from the Kingdom. Also it is the first report of OXA-72 and NDM-1 in A. baumannii in Saudi Arabia, and the coexistence of blaOXA-23 and blaNDM-1 genes in this species in the country. Awareness of the role of international travel in the spread of carbapenem-resistant determinants in the Kingdom, as well as effective infection control interventions in hospitals and strict antimicrobial stewardship in healthcare facilities and the community are keys to combat the rise of carbapenemase producers in the Kingdom.
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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 | 0.000 |
| 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".