Given a passage with a context and an answer, 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.

Q: Context: Hypertrophic cardiomyopathy (HCM) patients are at risk of ventricular arrhythmias (VAs). We aimed to explore whether systolic function by strain echocardiography is related to VAs and to the extent of fibrosis by cardiac magnetic resonance imaging (CMR).', 'We included 150 HCM patients and 50 healthy individuals. VAs were defined as non-sustained and sustained ventricular tachycardia and aborted cardiac arrest. Left ventricular function was assessed by ejection fraction (EF) and by global longitudinal strain (GLS) assessed by speckle tracking echocardiography. Mechanical dispersion was calculated as standard deviation (SD) of time from Q/R on ECG to peak longitudinal strain in 16 left ventricular segments. Late gadolinium enhancement (LGE) was assessed by CMR. HCM patients had similar EF (61 ± 5% vs. 61 ± 8%, P = 0.77), but worse GLS (-15.7 ± 3.6% vs. -21.1 ± 1.9%, P < 0.001) and more pronounced mechanical dispersion (64 ± 22 vs. 36 ± 13 ms, P < 0.001) compared with healthy individuals. VAs were documented in 37 (25%) HCM patients. Patients with VAs had worse GLS (-14.1 ± 3.6% vs. -16.3 ± 3.4%, P < 0.01), more pronounced mechanical dispersion (79 ± 27 vs. 59 ± 16 ms, P < 0.001), and higher %LGE (6.1 ± 7.8% vs. 0.5 ± 1.4%, P < 0.001) than patients without VAs. Mechanical dispersion correlated with %LGE (R = 0.52, P < 0.001) and was independently associated with VAs (OR 1.6, 95% CI 1.1-2.3, P = 0.02) and improved risk stratification for VAs.
Answer: GLS, mechanical dispersion, and LGE were markers of VAs in HCM patients. Mechanical dispersion was a strong independent predictor of VAs and related to the extent of fibrosis. Strain echocardiography may improve risk stratification of VAs in HCM.

A: Is strain echocardiography related to fibrosis and ventricular arrhythmias in hypertrophic cardiomyopathy?
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Q: Context: Diabetic cardiomyopathy (DCM) is associated with suppressed autophagy and augmented apoptosis in the heart although the interplay between the two remains elusive. The ability of mammalian sterile 20-like kinase 1 to regulate both autophagy and apoptosis prompted us to investigate it as a possible candidate in the progression of DCM.', 'Wild-type, Mst1 (also known as Stk4) transgenic and Mst1-knockout mice were challenged with streptozotocin to induce experimental diabetes. In addition, cultured neonatal mouse cardiomyocytes were subjected to simulated diabetes to probe mechanisms.', 'Mst1 knockout alleviated while Mst1 overexpression aggravated cardiac dysfunction in diabetes. Diabetic Mst1 transgenic mice exhibited decreased LC3 expression and enhanced protein aggregation. In contrast, typical autophagosomes were observed in diabetic Mst1-knockout mice with increased LC3 expression and reduced protein aggregation. Mst1 downregulation promoted autophagic flux as demonstrated by increased LC3-II and decreased p62 expression in the presence of bafilomycin A1. Furthermore, Mst1 overexpression increased, while Mst1 knockout decreased, cardiomyocyte apoptosis both in vivo and in vitro. Co-immunoprecipitation assays showed that Mst1 overexpression promoted Beclin1 binding to B cell lymphoma 2 (Bcl-2) and induced dissociation of Bcl-2 from Bax in diabetic mice. Conversely, Mst1 knockout disrupted the Beclin1-Bcl-2 complex and enhanced the interaction between Bcl-2 and Bax.
Answer: Mst1 knockout restores autophagy and protects against apoptosis in cardiomyocytes, en route to the rescue against DCM.

A: Does mST1 coordinately regulate autophagy and apoptosis in diabetic cardiomyopathy in mice?
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Q: Context: Glioblastoma (GBM) tumors exhibit strong phenotypic differences that can be quantified using magnetic resonance imaging (MRI), but the underlying biological drivers of these imaging phenotypes remain largely unknown. An Imaging-Genomics analysis was performed to reveal the mechanistic associations between MRI derived quantitative volumetric tumor phenotype features and molecular pathways.', 'One hundred fourty one patients with presurgery MRI and survival data were included in our analysis. Volumetric features were defined, including the necrotic core (NE), contrast-enhancement (CE), abnormal tumor volume assessed by post-contrast T1w (tumor bulk or TB), tumor-associated edema based on T2-FLAIR (ED), and total tumor volume (TV), as well as ratios of these tumor components. Based on gene expression where available (n\u2009=\u200991), pathway associations were assessed using a preranked gene set enrichment analysis. These results were put into context of molecular subtypes in GBM and prognostication.', 'Volumetric features were significantly associated with diverse sets of biological processes (FDR\u2009<\u20090.05). While NE and TB were enriched for immune response pathways and apoptosis, CE was associated with signal transduction and protein folding processes. ED was mainly enriched for homeostasis and cell cycling pathways. ED was also the strongest predictor of molecular GBM subtypes (AUC\u2009=\u20090.61). CE was the strongest predictor of overall survival (C-index\u2009=\u20090.6; Noether test, p\u2009=\u20094x10(-4)).
Answer: GBM volumetric features extracted from MRI are significantly enriched for information about the biological state of a tumor that impacts patient outcomes. Clinical decision-support systems could exploit this information to develop personalized treatment strategies on the basis of noninvasive imaging.

A:
Does imaging-genomics reveal driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma?
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