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.

Example Input: Context: No formal assessment of life expectancy in women with BRCA1 and BRCA2 mutations in these genes has been reported previously. We have evaluated life expectancy using actuarial analysis and assessed the effect of breast and ovarian cancers on premature death in >1,000 BRCA1/2 carriers.', 'Families with pathogenic mutations in BRCA1 and BRCA2 have been ascertained in a 10-million population region of United Kingdom since 1996. Mutation carriers and their first-degree relatives were used in an analysis of breast and ovarian cancer incidence and mortality as well as to derive and compare an actuarial assessment of life expectancy.', 'Six hundred twelve BRCA1 and 482 BRCA2 female mutation carriers were identified from 482 families. Life expectancy was significantly reduced for BRCA1 carriers compared with BRCA2 (P = 0.0002). This effect was attributable to an increased death rate from ovarian cancer (P = 0.04). Kaplan-Meier analysis revealed a better long-term survival from early-stage ovarian cancer in BRCA2 carriers but no significant differences in deaths from breast cancer or from women presenting with late-stage ovarian cancer. There was no other major contributing cause to death other than breast/ovarian cancer in BRCA1/2 female carriers.\Question: Is better life expectancy in women with BRCA2 compared with BRCA1 mutations attributable to lower frequency and later onset of ovarian cancer?
Example Output: Interventions to reduce ovarian cancer incidence are likely to have a greater effect on life expectancy in BRCA1 compared with BRCA2 carriers.

Example Input: Context: Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors. Despite the advances in therapy over the years, its mortality remains high. The aim of this study was to evaluate the expression of small ubiquitin-like modifier (SUMO) proteases 1 (SENP1) in NSCLC tissues and its role in the regulation of vascular endothelial growth factor (VEGF) expression. We also investigated the association between the expression level of SENP1 and the clinicopathological features and survival of the patients.', 'A SENP1 small interfering RNA (siRNA) was constructed and transfected into the NSCLC cells. VEGF gene expression was analyzed by real-time polymerase chain reaction (RT-PCR). Immunohistochemistry staining was used to assess the expression of SENP1 in 100 NSCLC patients and its association with the clinicopathological features and survival was analyzed.', 'VEGF expression was significantly higher in NSCLC tissues than in normal lung tissues. Inhibition of SENP1 by siRNA was associated with decreased VEGF expression. SENP1 was over-expressed in 55 of the 100 NSCLC samples (55%) and was associated with a moderate and low histological tumor grade (3.6%, 38.2%, and 58.2% in high, moderate and low differentiated tumors, respectively, P = 0.046), higher T stage (10.9% in T1, and 89.1% in T2 and T3 tumor samples, P < 0.001) and TNM stage (10.9% in stage I, and 89.1% in stages II and III tumor samples, P < 0.001). The rate of lymph node metastasis was significantly higher in the SENP1 over-expression group (76.4%) than that in the SENP1 low expression group (33.3%, P < 0.001). Sixty three patients received postoperative chemotherapy, including 34 with SENP1 over-expression and 29 with SENP1 low expression. Among the 34 patients with SENP1 over-expression, 22 (64.7%) patients developed recurrence or metastasis, significantly higher than those in the low expression group 27.6% (8/29) (P = 0.005). Multivariate Cox regression analysis showed that lymph node metastasis (P = 0.015), TNM stage (P = 0.001), and SENP1 expression level (P = 0.002) were independent prognostic factors for the survival of NSCLC patients.\Question: Does over-expression of small ubiquitin-like modifier proteases 1 predict chemo-sensitivity and poor survival in non-small cell lung cancer?
Example Output: SENP1 may be a promising predictor of survival, a predictive factor of chemo-sensitivity for NSCLC patients, and potentially a desirable drug target for lung carcinoma target therapy.

Example Input: Context: Differentiating bipolar disorder (BD) from major depressive disorder (MDD) often poses a major clinical challenge, and optimal clinical care can be hindered by misdiagnoses. This study investigated the differences between BD and MDD in resting-state functional network connectivity (FNC) using a data-driven image analysis method.', 'In this study, fMRI data were collected from unmedicated subjects including 13 BD, 40 MDD and 33 healthy controls (HC). The FNC was calculated between functional brain networks derived from fMRI using group independent component analysis (ICA). Group comparisons were performed on connectivity strengths and other graph measures of FNC matrices.', 'Statistical tests showed that, compared to MDD, the FNC in BD was characterized by more closely connected and more efficient topological structures as assessed by graph theory. The differences were found at both the whole-brain-level and the functional-network-level in prefrontal networks located in the dorsolateral/ventrolateral prefrontal cortex (DLPFC, VLPFC) and anterior cingulate cortex (ACC). Furthermore, interconnected structures in these networks in both patient groups were negatively associated with symptom severity on depression rating scales.\Question: Does resting-state functional network connectivity in prefrontal regions differ between unmedicated patients with bipolar and major depressive disorders?
Example Output:
As patients were unmedicated, the sample sizes were relatively small, although they were comparable to those in previous fMRI studies comparing BD and MDD.