TASK DEFINITION: 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.
PROBLEM: Context: The effect of the interaction between type 2 diabetes and dyslipidemia on inflammation and lipid peroxidation (LPO) has not been assessed.', 'To investigate whether diabetes coupled with dyslipidemia alters oxidative metabolism leading to increased LPO products and inflammatory status.', '100 patients were divided into four groups based upon diabetic and dyslipidemic status: poorly controlled diabetes with dyslipidemia (DM-PC/D), well-controlled diabetes with dyslipidemia (DM-WC/D), normoglycemic individuals with dyslipidemia (NG/D), and normoglycemic individuals without dyslipidemia (NG/ND). Plasma was evaluated for an LPO product (MDA), antioxidant levels and inflammatory cytokines.', 'Diabetics presented significantly higher levels of LPO (p<0.05) and the DM-PC/D had higher levels of proinflammatory cytokines and MDA in the plasma in comparison with normoglycemics (p<0.05). Interestingly IL1-, IL-6, and TNF- in DM-WC/D were not statistically different from those in DM-PC/D. Normoglycemic individuals with dyslipidemia presented significantly increased levels of IL-6 and TNF- when compared to normoglycemic without dyslipidemia (p<0.05). MDA levels were also positively correlated with the presence of DM complications (r=0.42, p<0.01).\Question: Are diabetes and increased lipid peroxidation associated with systemic inflammation even in well-controlled patients?

SOLUTION: These findings show that dyslipidemia is associated with an increased inflammatory status, even in well-controlled diabetics and in normoglycemics. Our results suggest that lipid metabolism and peroxidation are important for the development of inflammation, which is elevated in several complications associated with diabetes.

PROBLEM: 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?

SOLUTION: 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.

PROBLEM: Context: This study assesses the relationship between septal curvature and mean pulmonary artery pressure and indexed pulmonary vascular resistance in children with pulmonary hypertension. We hypothesized that septal curvature could be used to estimate right ventricular afterload and track acute changes in pulmonary hemodynamics.', 'Fifty patients with a median age of 6.7 years (range, 0.45-16.5 years) underwent combined cardiac catheterization and cardiovascular magnetic resonance. The majority had idiopathic pulmonary arterial hypertension (n=30); the remaining patients had pulmonary hypertension associated with repaired congenital heart disease (n=17) or lung disease (n=3). Mean pulmonary artery pressure and pulmonary vascular resistance were acquired at baseline and during vasodilation. Septal curvature was measured using real-time cardiovascular magnetic resonance. There was a strong correlation between mean pulmonary artery pressure and SCmin at baseline and during vasodilator testing (r=-0.81 and -0.85, respectively; P<0.01). A strong linear relationship also existed between pulmonary vascular resistance and minimum septal curvature indexed to cardiac output both at baseline and during vasodilator testing (r=-0.88 and -0.87, respectively; P<0.01). Change in septal curvature metrics moderately correlated with absolute change in mean pulmonary artery pressure and pulmonary vascular resistance, respectively (r=0.58 and -0.74; P<0.01). Septal curvature metrics were able to identify vasoresponders with a sensitivity of 83% (95% confidence interval, 0.36-0.99) and a specificity of 91% (95% confidence interval, 0.77-0.97), using the Sitbon criteria. Idiopathic pulmonary arterial hypertension subgroup analysis revealed 3 responders with SCmin values of 0.523, 0.551, and 0.568. If the middle value of 0.551 is taken as a cutoff, the approximate sensitivity would be 67% and the specificity would be 93%.\Question: Does real-time magnetic resonance assessment of septal curvature accurately track acute hemodynamic changes in pediatric pulmonary hypertension?

SOLUTION:
Septal curvature metrics are able to estimate right ventricular afterload and track acute changes in pulmonary hemodynamics during vasodilator testing. This suggests that septal curvature could be used for continuing assessment of load in pulmonary hypertension.