In this task, you are given an abstract of article. Your task is to generate label "True" if abstract is structured, otherwise generate "False". A structured abstract is composed of a topic sentence (or key sentence), relevant supporting sentences, and a closing (or transition) sentence. This structure is key to keeping your abstract focused on the main idea and creating a clear and concise image.

[EX Q]: High breast cancer mortality rates have been reported in the northeastern part of the United States, with recent attention focused on Long Island, New York. In this study, the authors investigate whether the high breast cancer mortality is evenly spread over the Northeast, in the sense that any observed clusters of deaths can be explained by chance alone, or whether there are clusters of statistical significance. Demographic data and age-specific breast cancer mortality rates for women were obtained for all 244 counties in 11 northeastern states and for the District of Columbia for 1988-1992. A recently developed spatial scan statistic is used, which searches for clusters of cases without specifying their size or location ahead of time, and which tests for their statistical significance while adjusting for the multiple testing inherent in such a procedure. The basic analysis is adjusted for age, with further analyses examining how the results are affected by incorporating race, urbanicity, and parity as confounding variables. There is a statistically significant and geographically broad cluster of breast cancer deaths in the New York City-Philadelphia, Pennsylvania, metropolitan area (p = 0.0001), which has a 7.4% higher mortality rate than the rest of the Northeast. The cluster remains significant when race, urbanicity, and/or parity are included as confounding variables. Four smaller subclusters within this area are also significant on their own strength: Philadelphia with suburbs (p = 0.0001), Long Island (p = 0.0001), central New Jersey (p = 0.0001), and northeastern New Jersey (p = 0.0001). The elevated breast cancer mortality on Long Island might be viewed less as a unique local phenomenon and more as part of a more general situation involving large parts of the New York City-Philadelphia metropolitan area. The several known and hypothesized risk factors for which we could not adjust and that may explain the detected cluster are most notably age at first birth, age at menarche, age at menopause, breastfeeding, genetic mutations, and environmental factors.
[EX A]: False

[EX Q]: BACKGROUND & AIMS Hepatic stellate cell activation is a wound-healing response to liver injury. However, continued activation of stellate cells during chronic liver damage causes excessive matrix deposition and the formation of pathological scar tissue leading to fibrosis and ultimately cirrhosis. The importance of sustained stellate cell activation for this pathological process is well recognized, and several signalling pathways that can promote stellate cell activation have been identified, such as the TGF-, PDGF-, and LPS-dependent pathways. However, the mechanisms that trigger and drive the early steps in activation are not well understood.
METHODS AND RESULTS We identified the Hippo pathway and its effector YAP as a key pathway that controls stellate cell activation. YAP is a transcriptional co-activator and we found that it drives the earliest changes in gene expression during stellate cell activation. Activation of stellate cells in vivo by CCl4 administration to mice or activation in vitro caused rapid activation of YAP as revealed by its nuclear translocation and by the induction of YAP target genes. YAP was also activated in stellate cells of human fibrotic livers as evidenced by its nuclear localization. Importantly, knockdown of YAP expression or pharmacological inhibition of YAP prevented hepatic stellate cell activation in vitro and pharmacological inhibition of YAP impeded fibrogenesis in mice.
CONCLUSIONS YAP activation is a critical driver of hepatic stellate cell activation and inhibition of YAP presents a novel approach for the treatment of liver fibrosis.
[EX A]: True

[EX Q]: OBJECTIVE To examine the safety of using aliskiren combined with agents used to block the renin-angiotensin system.
DESIGN Systematic review and meta-analysis of randomised controlled trials.
DATA SOURCES Medline, Embase, the Cochrane Library, and two trial registries, published up to 7 May 2011. STUDY SELECTION Published and unpublished randomised controlled trials that compared combined treatment using aliskiren and angiotensin converting enzyme inhibitors or angiotensin receptor blockers with monotherapy using these agents for at least four weeks and that provided numerical data on the adverse event outcomes of hyperkalaemia and acute kidney injury. A random effects model was used to calculate pooled risk ratios and 95% confidence intervals for these outcomes.
RESULTS 10 randomised controlled studies (4814 participants) were included in the analysis. Combination therapy with aliskiren and angiotensin converting enzyme inhibitors or angiotensin receptor blockers significantly increased the risk of hyperkalaemia compared with monotherapy using angiotensin converting enzymes or angiotensin receptor blockers (relative risk 1.58, 95% confidence interval 1.24 to 2.02) or aliskiren alone (1.67, 1.01 to 2.79). The risk of acute kidney injury did not differ significantly between the combined therapy and monotherapy groups (1.14, 0.68 to 1.89).
CONCLUSION Use of aliskerin in combination with angiotensin converting enzyme inhibitors or angiotensin receptor blockers is associated with an increased risk for hyperkalaemia. The combined use of these agents warrants careful monitoring of serum potassium levels.
[EX A]:
True