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]: To investigate the mechanisms of anti-atherosclerotic action of ezetimibe in rat vascular smooth muscle cells (VSMCs) in vitro.', 'VSMCs of SD rats were cultured in the presence of Chol:MβCD (10 μg/mL) for 72 h, and intracellular lipid droplets and cholesterol levels were evaluated using Oil Red O staining, HPLC and Enzymatic Fluorescence Assay, respectively. The expression of caveolin-1, sterol response element-binding protein-1 (SREBP-1) and ERK1/2 were analyzed using Western blot assays. Translocation of SREBP-1 and ERK1/2 was detected with immunofluorescence.', 'Treatment with Chol:MβCD dramatically increased the cellular levels of total cholesterol (TC), cholesterol ester (CE) and free cholesterol (FC) in VSMCs, which led to the formation of foam cells. Furthermore, Chol:MβCD treatment significantly decreased the expression of caveolin-1, and stimulated the expression and nuclear translocation of SREBP-1 in VSMCs. Co-treatment with ezetimibe (3 μmol/L) significantly decreased the cellular levels of TC, CE and FC, which was accompanied by elevation of caveolin-1 expression, and by a reduction of SREBP-1 expression and nuclear translocation. Co-treatment with ezetimibe dose-dependently decreased the expression of phosphor-ERK1/2 (p-ERK1/2) in VSMCs. The ERK1/2 inhibitor PD98059 (50 μmol/L) altered the cholesterol level and the expression of p-ERK1/2, SREBP-1 and caveolin-1 in the same manner as ezetimibe did.
[EX A]: Does ezetimibe suppress cholesterol accumulation in lipid-loaded vascular smooth muscle cells in vitro via MAPK signaling?

[EX Q]: To understand whether the relationship between young children's autonomic nervous system (ANS) responses predicted their BMI, or vice versa, the association between standardized BMI (zBMI) at 2, 3.5, and 5 years of age and ANS reactivity at 3.5-5 years of age, and whether zBMI predicts later ANS reactivity or whether early ANS reactivity predicts later zBMI, was studied.", "Low-income, primarily Latino children (n=112) were part of a larger cohort study of mothers recruited during early pregnancy. Study measures included maternal prenatal weight, children's health behaviors (i.e., time watching television, fast food consumption, and time playing outdoors), children's height and weight at 2, 3.5, and 5 years, and children's ANS reactivity at 3.5 and 5 years. ANS measures of sympathetic nervous system (i.e., pre-ejection period) and parasympathetic nervous system (i.e., respiratory sinus arrhythmia) activity were monitored during rest and four challenges. Reactivity was calculated as the difference between mean challenge response and rest. Structural equation models analyzed the relationship between children's zBMI at 2, 3.5, and 5 years and ANS reactivity at 3.5 and 5 years, adjusting for mother's BMI, children's behaviors, and changes in height.", 'There was no association between zBMI and ANS cross-sectionally. Children with high zBMI at 2 or 3.5 years or large zBMI increases from 2 to 3.5 years of age had decreased sympathetic activity at 5 years. Neither sympathetic nor parasympathetic reactivity at 3.5 years predicted later zBMI.
[EX A]: Does latino children 's body mass index at 2-3.5 years predict sympathetic nervous system activity at 5 years?

[EX Q]: Prostate cancer is one of the most common complex diseases with high leading cause of death in men. Identifications of prostate cancer associated genes and biomarkers are thus essential as they can gain insights into the mechanisms underlying disease progression and advancing for early diagnosis and developing effective therapies.', 'In this study, we presented an integrative analysis of gene expression profiling and protein interaction network at a systematic level to reveal candidate disease-associated genes and biomarkers for prostate cancer progression. At first, we reconstructed the human prostate cancer protein-protein interaction network (HPC-PPIN) and the network was then integrated with the prostate cancer gene expression data to identify modules related to different phases in prostate cancer. At last, the candidate module biomarkers were validated by its predictive ability of prostate cancer progression.', 'Different phases-specific modules were identified for prostate cancer. Among these modules, transcription Androgen Receptor (AR) nuclear signaling and Epidermal Growth Factor Receptor (EGFR) signalling pathway were shown to be the pathway targets for prostate cancer progression. The identified candidate disease-associated genes showed better predictive ability of prostate cancer progression than those of published biomarkers. In context of functional enrichment analysis, interestingly candidate disease-associated genes were enriched in the nucleus and different functions were encoded for potential transcription factors, for examples key players as AR, Myc, ESR1 and hidden player as Sp1 which was considered as a potential novel biomarker for prostate cancer.
[EX A]:
Does integrative analysis reveal disease-associated genes and biomarkers for prostate cancer progression?