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.

Example Input: Psychomotor abnormalities characterize both unipolar (UP) depression and bipolar (BP) depression. We aimed to assess their neurobiological correlates in terms of motor activity (AL) and resting-state cerebral blood flow (rCBF) and investigate their association in BP, UP, and healthy controls (HC).', 'We enrolled 42 depressed patients (22 BP, 20 UP) and 19 HC matched for age, gender, education, income. AL and rCBF were objectively assessed with the use of wrist actigraphy and arterial spin labeling. Group differences and the association of AL and rCBF were computed.', 'Activity level was significantly reduced in patients, but no difference was found between BP and UP. Increased perfusion was found in BP compared with UP and HC, in multiple brain areas. We found positive correlations of rCBF and AL in BP and UP, in different parts of the insula and frontal regions. Only BP showed a cluster in the left precentral gyrus. In HC, only inverse correlations of AL and rCBF were found.
Example Output: Do distinct resting-state perfusion patterns underlie psychomotor retardation in unipolar vs. bipolar depression?

Example Input: Body weight correlates with risk of breast cancer death.', 'A retrospective cohort study using patient medical records, electronic cancer registry data, and archived tissue specimens.', 'A 395-bed, comprehensive community hospital.', 'One thousand three hundred seventy-six women, aged 24 to 81 years, who were diagnosed with breast cancer between January 1, 1988, and December 31, 1995, and for whom complete medical records and adequate tissue specimens existed.', "Body weight at the time of diagnosis and patient status (ie, alive and free of breast cancer, living with breast cancer, dead of breast cancer, or dead of other cause) at the time of longest follow-up. Additional data collected, including age at diagnosis, menopausal status, tumor size, tumor grade, lymph node status, stage at diagnosis, race, estrogen-receptor (ER) status, and treatment information, were used to create multivariate Cox proportional hazards models to estimate hazard rate (HR) ratios and 95% confidence intervals (CIs) for breast cancer death. We collected ER status from the patients' medical records, when available, and supplemented the information by using immunohistochemical techniques to determine ER status from archived paraffin-embedded tumor blocks.", 'Patients were followed up for a median of 6.8 years after diagnosis. Two hundred forty-six patients died from breast cancer. Among patients with early-stage disease (I and IIA), we observed a dose-response relationship of increasing weight with increasing likelihood of dying of breast cancer. Compared with women in the lowest category of weight (< 133 lb [60 kg] at diagnosis), women in the highest category (> or = 175 lb [79 kg]) experienced a 2.5-fold increased risk of dying from breast cancer (HR ratio, 2.54 [95% CI, 1.08-6.00]; trend P = .02). Women with ER-negative cancer experienced an approximately 2-fold higher risk of dying from breast cancer compared with women with ER-positive cancer, regardless of stage at diagnosis. Women in the upper 50th percentile of weight with early-stage disease and with ER-negative tumors had a nearly 5-fold increased risk of dying (HR ratio, 4.99 [95% CI, 2.17-11.48]; P for interaction = .10) compared with women in the lower 50th percentile of weight and ER-positive tumors. The results were similar for body mass index, a measure of obesity in which weight is adjusted for height.
Example Output: Does body weight correlate with mortality in early-stage breast cancer?

Example Input: Obesity is frequently associated with cirrhosis, and cirrhotic patients may develop simultaneous loss of skeletal muscle and gain of adipose tissue, culminating in the condition of sarcopenic obesity. Additionally, muscle depletion is characterized by both a reduction in muscle size and increased proportion of muscular fat, termed myosteatosis. In this study, we aimed to establish the frequency and clinical significance of sarcopenia, sarcopenic obesity and myosteatosis in cirrhotic patients.', 'We analysed 678 patients with cirrhosis. Sarcopenia, sarcopenic obesity and myosteatosis were analysed by CT scan using the third lumbar vertebrae skeletal muscle and attenuation indexes, using previously validated gender-and body mass index-specific cutoffs.', 'Patients were predominately men (n=457, 67%), and cirrhosis aetiology was hepatitis C virus in 269 patients (40%), alcohol in 153 (23%), non-alcoholic steatohepatitis/cryptogenic in 96 (14%), autoimmune liver disease in 55 (8%), hepatitis B virus in 43 (6%), and others in 5 patients (1%). Sarcopenia was present in 292 (43%), 135 had sarcopenic obesity (20%) and 353 had myosteatosis (52%). Patients with sarcopenia (223 vs. 9522months, P<0.001), sarcopenic obesity (223 vs. 9522months, P<0.001), and myosteatosis (285 vs. 9522months, P<0.001) had worse median survival than patients without muscular abnormalities. By multivariate Cox regression analysis, both sarcopenia [hazard ratio (HR) 2.00, 95% confidence interval (CI) 1.44-2.77, P<0.001], and myosteatosis (HR 1.42, 95% CI 1.02-1.07, P=0.04) were associated with mortality.
Example Output:
Are sarcopenic obesity and myosteatosis associated with higher mortality in patients with cirrhosis?