Given a passage, construct a question on the basis of the information present in the passage. Construct the question in such a way that (i) it is unambiguous, (ii) it is answerable from the passage, (iii) its answer is unique (iv) it answer uses text phrases from the passage. Avoid creating questions that (i) can't be answered correctly without actually understanding the passage and (ii) uses the same words or phrases given in the passage.

[EX Q]: Approximately 50% of subjects with essential hypertension (EH) are insulin resistant, and this defect in insulin action could contribute to increased cardiovascular disease (CVD) risk in these patients. To test this hypothesis, we attempted to see if there was a link between insulin resistance (IR) and carotid intimal medial thickness (IMT), an early index of CVD, in patients with essential hypertension.', 'Ultrasound quantification of carotid IMT was performed in 79 hypertensive patients, and 63 patients (31 m and 32 f), defined as being free of plaque (IMT < 1.3 mm), were further subdivided into normal (<1.0 mm) and thickened (1-1.3 mm) IMT groups. Subjects in the thickened IMT group were older and had significantly (p < 0.05) higher plasma concentrations of fasting insulin, nitric oxide (NO(x)) and intercellular adhesion molecule 1 (ICAM-1). However, the two groups were not significantly different in terms of blood pressure, overall or regional obesity, fasting lipid levels, uric acid, concentrations of other cellular adhesion molecules or levels of C-reactive protein. There were significant (p < 0.05) correlations in the whole population between IMT and age, fasting insulin and NO(x), and multiple regression analysis identified fasting insulin as an independent predictor of IMT.
[EX A]: Do insulin resistance/compensatory hyperinsulinemia predict carotid intimal medial thickness in patients with essential hypertension?

[EX Q]: The purpose of this study was to evaluate the impact of FDG-PET following chemoradiotherapy (CRT) on treatment planning and survival in patients with oesophageal cancer (OC).', 'Fifty-three consecutive OC patients had a post-treatment PET scan to evaluate tumour response to CRT prior to possible surgery. Baseline pre-CRT PET was performed in 33 patients. Prospectively recorded post-CRT management plans were compared with post-PET treatment. High impact was defined as a change in treatment intent or modality. Survival was analysed using the Kaplan-Meier product limit method and Cox proportional hazards regression model.', 'After completion of CRT, 23/53 patients (43%) achieved complete metabolic response (CMR), as compared with only four (8%) with complete response on computed tomography. High PET impact was observed in 19 patients (36%). CMR was strongly predictive of survival (p<0.008) on multivariate analysis. CMR patients in whom resection was not performed had comparable survival to those (CMR and non-CMR) who underwent resection.
[EX A]: Does fDG-PET status following chemoradiotherapy provide high management impact and powerful prognostic stratification in oesophageal cancer?

[EX Q]: Network component analysis (NCA) became a popular tool to understand complex regulatory networks. The method uses high-throughput gene expression data and a priori topology to reconstruct transcription factor activity profiles. Current NCA algorithms are constrained by several conditions posed on the network topology, to guarantee unique reconstruction (termed compliancy). However, the restrictions these conditions pose are not necessarily true from biological perspective and they force network size reduction, pruning potentially important components.', 'To address this, we developed a novel, Iterative Sub-Network Component Analysis (ISNCA) for reconstructing networks at any size. By dividing the initial network into smaller, compliant subnetworks, the algorithm first predicts the reconstruction of each subnetwork using standard NCA algorithms. It then subtracts from the reconstruction the contribution of the shared components from the other subnetwork. We tested the ISNCA on real, large datasets using various NCA algorithms. The size of the networks we tested and the accuracy of the reconstruction increased significantly. Importantly, FOXA1, ATF2, ATF3 and many other known key regulators in breast cancer could not be incorporated by any NCA algorithm because of the necessary conditions. However, their temporal activities could be reconstructed by our algorithm, and therefore their involvement in breast cancer could be analyzed.
[EX A]:
Does iterative sub-network component analysis enable reconstruction of large scale genetic networks?