<i><scp>S</scp>taphylococcus aureus</i> nasal carriage in a <scp>M</scp>oroccan dialysis center and isolates characterization
Bibliographic record
Abstract
Staphylococcus aureus, which has its ecological niche in the anterior nares, has been shown to cause a variety of infectious diseases mainly for patients in hemodialysis units. We performed this study to evaluate the prevalence of nasal S. aureus carriage among hemodialysis outpatients, to determine the antimicrobial susceptibility of isolates, to characterize the virulence genes, and to identify associated risk factors. Nares swab specimens were obtained from 70 outpatients on hemodialysis between March and June 2010. Samples were plated immediately onto S. aureus specific media and pattern of antibacterial sensitivity was determined using disk diffusion method. Polymerase chain reaction was used to detect nuc, mecA, and genes encoding staphylococcal toxins. Medical record of patients was explored to determine S.aureus carriage risk factors. Nasal screening identified 42.9% S. aureus carriers with only one (3.3%) methicillin-resistant S. aureus isolate. Among the methicillin-susceptible S. aureus isolates, high rate of penicillin resistance (81.8%) has been detected. The identified risk factors were male gender and age ≤ 30 years. Research of virulence factors showed a high genetic diversity among the 30 S. aureus isolates. Twenty-one (70%) of them had at least one virulence gene, of which 3.3% were Panton-Valentine leukocidin (lukS/F-PV) genes. S. aureus carriage must be screened for at regular intervals in hemodialysis patients. Setting up a bacterial surveillance system is one of the strategies to understand the epidemiology of methicillin-resistant S. aureus, to guide local antibiotic policy and prevent spread of antibiotic-resistant S. aureus.
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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 | 0.001 | 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".