Methicillin-Resistant<i>Staphylococcus aureus</i>Carriage among Students at a Historically Black University: A Case Study
Bibliographic record
Abstract
Background. Black people in the USA is afflicted with a higher rate of methicillin-resistant Staphylococcus aureus (MRSA) infection. This study determined the prevalence of MRSA carriage among black college students at a university setting. Methods. Hand and nasal swabs were collected and screened for MRSA by mannitol fermentation, coagulase, and DNase activities and their resistance to oxacillin. MRSA isolates were analyzed for antimicrobial resistance pattern, genetic profile for staphylococcal cassette chromosome mec (SCCmec) type, pulsed-field type, multilocus sequence type (ST), and the presence of Panton-Valentine leukocidin (PVL) gene. Results. MRSA was isolated from 1 of the 312 (0.3%) hand swabs and 2 of the 310 (0.65%) nasal swabs, respectively. All isolates lack multidrug resistance and have type IV SCCmec, characteristic of community-associated MRSA. These isolates were a ST8-MRSA-IVa-PVL(+) (USA300 strain), a ST8-MRSA-IVb-PVL(-), and a new MLST, ST2562-MRSA-IV-PVL(-), identified in this study. These isolates were thus not transmitted among students. Conclusion. We found a low rate of MRSA carriage among students in a black university. Our finding highlights the need of future study which involves multiinstitutions and other ethnic group to assess the association of black race with MRSA carriage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".