Epidemiological and Inducible Resistance in Coagulase Negative Staphylococci
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: Coagulase negative staphylococci (CNS) are potential pathogens with the increased use of implants in hospitals. Macrolide, lincosamide and streptogramin B (MLSB) are used in the treatment of staphylococcal infections. The aim of this study was to molecular detection of inducible clindamycin resistance and genetic pattern in CNS isolates and their transmission between hospitals. MATERIALS & METHODS: 110 CNS strains, isolated from hospitalized patients in the intensive care unit and infectious wards of Besat and Toohid hospitals, Sanandaj. Methicillin resistance was done by agar screen test and the resistance inducible Clindamycin by the D-Test. Multiplex PCR was performed, using primers specific for erm (A, B, C, and TR) genes. Diversity of strains was determined by ERIC-PCR technique based on the similarities between DNA fingerprints by using Jaccards coefficient in the SAHN program of the NTSYS-pc software. RESULTS: Of the 110 isolates, 64(58.2%) were methicillin -resistant CNS (MRCNS), 48(43.6%) were resistant to erythromycin (ERCNS). Out of 48 Erythromycin-resistant strains 5 (10.4%) were iMLS B phenotypes that 4 isolates showed genes erm by Multiplex PCR. The ERIC-PCR profiles allowed typing of the 110 isolates into 90 ERIC-types which were grouped into fourteen main clusters (C1-C14). CONCLUSION: The results of this study also showed that most of CNS isolated produced different genomic fingerprint patterns, therefore, source of infection is differen t.
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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 | 0.001 |
| 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) | 0.002 | 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".