In this task, you are given a passage which has a question and the context. You have to generate an answer to the question based on the information present in the context.

[Q]: Context: Methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-resistant coagulase-negative staphylococci (MRCoNS) as important human pathogens are causes of nosocomial infections worldwide. Burn patients are at a higher risk of local and systemic infections with these microorganisms.', 'A screening method for MRSA by using a multiplex polymerase chain reaction (PCR) targeting the 16S ribosomal RNA (rRNA), mecA, and nuc genes was developed. The aim of the present study was to investigate the potential of this PCR assay for the detection of MRSA strains in samples from burn patients.', 'During an 11-month period, 230 isolates (53.11%) of Staphylococcus spp. were collected from burn patients. The isolates were identified as S. aureus by using standard culture and biochemical tests. DNA was extracted from bacterial colonies and multiplex PCR was used to detect MRSA and MRCoNS strains.', 'Of the staphylococci isolates, 149 (64.9%) were identified as S. aureus and 81 (35.21%) were described as CoNS. Among the latter, 51 (62.97%) were reported to be MRCoNS. From the total S. aureus isolates, 132 (88.6%) were detected as MRSA and 17 (11.4%) were methicillin-susceptible S. aureus (MSSA). The presence of the mecA gene in all isolates was confirmed by using multiplex PCR as a gold standard method.\Question: Do identification of methicillin-resistant Staphylococcus aureus ( MRSA ) strains isolated from burn patients by multiplex PCR?
[A]: This study presented a high MRSA rate in the region under investigation. The 16S rRNA-mecA-nuc multiplex PCR is a good tool for the rapid characterization of MRSA strains. This paper emphasizes the need for preventive measures and choosing effective antimicrobials against MRSA and MRCoNS infections in the burn units.

[Q]: Context: To assess, in a retrospective cohort, urinary tract urothelial carcinoma (UT-UC) in patients with various stages of chronic kidney disease (CKD) and their clinicopathological features, as patients with end-stage renal disease (ESRD) have a higher incidence of UT-UC, but the relationship between early stages of CKD and characteristics of UT-UC are less well known.', 'The study included 267 patients with pathologically confirmed UT-UC from January 1994 to December 2006; all had a physical examination (blood pressure), and measurements of laboratory data (serum creatinine, serum haemoglobin) and pathological data. The glomerular filtration rate (GFR) was calculated using the Modification of Diet in Renal Disease equation. Patients were divided into three groups by individual GFR (mL/min), i.e. >60 (no/mild CKD), 30-60 (CKD stage 3) and <30 (CKD stage 4/5).', 'The CKD stages included 81 (30.3%) patients with none/mild CKD, 121 (45.3%) with CKD stage 3 and 65 (24.3%) with CKD stage 4/5. There was a significant and parallel increase in the frequency of UT-UC as CKD severity increased from none/mild CKD to stage 3 (11% vs 55%), and from CKD stage 3 to 4/5 (55% vs 71%; P < 0.05). Pathologically, the frequency of high-grade and high T stage UT-UC in patients with CKD stage 3 (90% and 35%, respectively) and CKD stage 4/5 (91% and 29%, respectively) were significantly greater than in the group with none/mild CKD (P < 0.001). Advanced age and more distant metastasis were independent risk factors for patient survival.\Question: Does the aggressiveness of urinary tract urothelial carcinoma increase with the severity of chronic kidney disease?
[A]: The aggressiveness of UT-UC increased with the severity of CKD, and this might have important clinical consequences.

[Q]: Context: RNA editing is the process whereby an RNA sequence is modified from the sequence of the corresponding DNA template. In the mitochondria of land plants, some cytidines are converted to uridines before translation. Despite substantial study, the molecular biological mechanism by which C-to-U RNA editing proceeds remains relatively obscure, although several experimental studies have implicated a role for cis-recognition. A highly non-random distribution of nucleotides is observed in the immediate vicinity of edited sites (within 20 nucleotides 5' and 3'), but no precise consensus motif has been identified.", "Data for analysis were derived from the the complete mitochondrial genomes of Arabidopsis thaliana, Brassica napus, and Oryza sativa; additionally, a combined data set of observations across all three genomes was generated. We selected datasets based on the 20 nucleotides 5' and the 20 nucleotides 3' of edited sites and an equivalently sized and appropriately constructed null-set of non-edited sites. We used tree-based statistical methods and random forests to generate models of C-to-U RNA editing based on the nucleotides surrounding the edited/non-edited sites and on the estimated folding energies of those regions. Tree-based statistical methods based on primary sequence data surrounding edited/non-edited sites and estimates of free energy of folding yield models with optimistic re-substitution-based estimates of approximately 0.71 accuracy, approximately 0.64 sensitivity, and approximately 0.88 specificity. Random forest analysis yielded better models and more exact performance estimates with approximately 0.74 accuracy, approximately 0.72 sensitivity, and approximately 0.81 specificity for the combined observations.\Question: Do simple statistical models predict C-to-U edited sites in plant mitochondrial RNA?
[A]:
Simple models do moderately well in predicting which cytidines will be edited to uridines, and provide the first quantitative predictive models for RNA edited sites in plant mitochondria. Our analysis shows that the identity of the nucleotide -1 to the edited C and the estimated free energy of folding for a 41 nt region surrounding the edited C are the most important variables that distinguish most edited from non-edited sites. However, the results suggest that primary sequence data and simple free energy of folding calculations alone are insufficient to make highly accurate predictions.