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.

Example Input: OBJECTIVE We recently demonstrated that primitive neural crest-derived (NC) cells migrate from the cardiac neural crest during embryonic development and remain in the heart as dormant stem cells, with the capacity to differentiate into various cell types, including cardiomyocytes. Here, we examined the migration and differentiation potential of these cells on myocardial infarction (MI).   
 METHODS AND RESULTS We obtained double-transgenic mice by crossing protein-0 promoter-Cre mice with Floxed-enhanced green fluorescent protein mice, in which the NC cells express enhanced green fluorescent protein. In the neonatal heart, NC stem cells (NCSCs) were localized predominantly in the outflow tract, but they were also distributed in a gradient from base to apex throughout the ventricular myocardium. Time-lapse video analysis revealed that the NCSCs were migratory. Some NCSCs persisted in the adult heart. On MI, NCSCs accumulated at the ischemic border zone area (BZA), which expresses monocyte chemoattractant protein-1 (MCP-1). Ex vivo cell migration assays demonstrated that MCP-1 induced NCSC migration and that this chemotactic effect was significantly depressed by an anti-MCP-1 antibody. Small NC cardiomyocytes first appeared in the BZA 2 weeks post-MI and gradually increased in number thereafter.   
 CONCLUSIONS These results suggested that NCSCs migrate into the BZA via MCP-1/CCR2 signaling and contribute to the provision of cardiomyocytes for cardiac regeneration after MI.
Example Output: True

Example Input: BACKGROUND It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure.   
 RESULTS I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance.   
 CONCLUSIONS I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.
Example Output: True

Example Input: CONTEXT The growth inhibitory effect of tamoxifen, which is used for the treatment of hormone receptor-positive breast cancer, is mediated by its metabolites, 4-hydroxytamoxifen and endoxifen. The formation of active metabolites is catalyzed by the polymorphic cytochrome P450 2D6 (CYP2D6) enzyme.   
 OBJECTIVE To determine whether CYP2D6 variation is associated with clinical outcomes in women receiving adjuvant tamoxifen.   
 DESIGN, SETTING, AND PATIENTS Retrospective analysis of German and US cohorts of patients treated with adjuvant tamoxifen for early stage breast cancer. The 1325 patients had diagnoses between 1986 and 2005 of stage I through III breast cancer and were mainly postmenopausal (95.4%). Last follow-up was in December 2008; inclusion criteria were hormone receptor positivity, no metastatic disease at diagnosis, adjuvant tamoxifen therapy, and no chemotherapy. DNA from tumor tissue or blood was genotyped for CYP2D6 variants associated with reduced (*10, *41) or absent (*3, *4, *5) enzyme activity. Women were classified as having an extensive (n=609), heterozygous extensive/intermediate (n=637), or poor (n=79) CYP2D6 metabolism.   
 MAIN OUTCOME MEASURES Time to recurrence, event-free survival, disease-free survival, and overall survival.   
 RESULTS Median follow-up was 6.3 years. At 9 years of follow-up, the recurrence rates were 14.9% for extensive metabolizers, 20.9% for heterozygous extensive/intermediate metabolizers, and 29.0% for poor metabolizers, and all-cause mortality rates were 16.7%, 18.0%, and 22.8%, respectively. Compared with extensive metabolizers, there was a significantly increased risk of recurrence for heterozygous extensive/intermediate metabolizers (time to recurrence adjusted hazard ratio [HR], 1.40; 95% confidence interval [CI], 1.04-1.90) and for poor metabolizers (time to recurrence HR, 1.90; 95% CI, 1.10-3.28). Compared with extensive metabolizers, those with decreased CYP2D6 activity (heterozygous extensive/intermediate and poor metabolism) had worse event-free survival (HR, 1.33; 95% CI, 1.06-1.68) and disease-free survival (HR, 1.29; 95% CI, 1.03-1.61), but there was no significant difference in overall survival (HR, 1.15; 95% CI, 0.88-1.51).   
 CONCLUSION Among women with breast cancer treated with tamoxifen, there was an association between CYP2D6 variation and clinical outcomes, such that the presence of 2 functional CYP2D6 alleles was associated with better clinical outcomes and the presence of nonfunctional or reduced-function alleles with worse outcomes.
Example Output:
True

