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.

Input: Consider Input: In this protocol, we describe how to isolate keratinocytes from adult mouse epidermis, fractionate them into different sub-populations on the basis of cell surface markers and examine their function in an in vivo skin reconstitution assay with disaggregated neonatal dermal cells. We also describe how the isolated keratinocytes can be subjected to clonal analysis in vitro and in vivo and how to enrich for hair follicle-inducing dermal papilla cells in the dermal preparation. Using these approaches, it is possible to compare the capacity of different populations of adult epidermal stem cells to proliferate and to generate progeny that differentiate along the different epidermal lineages. Isolating, fractionating and grafting cells for the skin reconstitution assay is normally spread over 2 d. Clonal growth in culture is assessed after 14 d, while evaluation of the grafts is carried out after 45 weeks.

Output: False

Input: Consider Input: BACKGROUND MicroRNAs (miRNAs) are small non-coding RNA molecules. Reduced or increased levels of specific miRNAs are observed in colon and other cancers, supporting their role in carcinogenesis. Detection of colorectal polyps is the cornerstone of the Bowel Cancer Screening Programme in the UK. However, uptake of screening nationally remains under 60%. We aimed to see whether circulating plasma miRNAs can be used to screen for patients with colorectal polyps, adenomas, or both.
METHODS Blood samples were taken from patients from the Bowel Cancer Screening Programme (asymptomatic but faecal occult blood testing [FOBt] positive). Plasma RNA was extracted, target miRNAs (19a, 98, 146b, 186, 191, 222*, 331-5p, 452, 625, 664, 1247) were identified on pooled case miRNA assay cards, and miRNA fraction was quantified by quantitative RT-PCR assay. Results were compared with endoscopy reports and with histology of any polyps identified and removed. Analysis was done with Excel (2011) and SPSS (version 20) software.
FINDINGS 210 patients were included (117 with polyps, 12 with cancer, 81 healthy controls [FOBt positive]). The miRNA panel showed significant differences in expression (on t testing) for patients compared with controls for those with polyps, cancer, or both (miR-19a, p=00184; miR-98, p=00206; miR-146b, p=00029; miR-186, p=00006; miR-62,5 p=00008), polyps (miR-19a, p=00233; miR-98, p=00224; miR-146b, p=0003; miR-186, p=00004; miR-625, p=0001), adenomas (miR-19a, p=00339; miR-98, p=00266; miR-146b, p=00045; miR-186, p=00008; miR-625, p=00049), multiple adenomas (both sides of colon; miR-146b, p=00194; miR-186, p=00226; miR-625, p=00013), and right-sided adenomas (miR-98, p=0031; miR-146b, p=00076; miR-186, p=00041; miR-331-5p, p=00142; miR-625, p=00049). Receiver operating characteristic analysis showed sensitivity of 60% or more, and specificity of 86% or more for men with polyps, men with adenomas, all patients with haemorrhoids or diverticulosis and polyps, and all patients with haemorrhoids or diverticulosis and adenomas.
INTERPRETATION The target miRNAs that we identified showed significant differences in expression levels for patients with polyps and patients with adenomas from controls. Use of this panel has potential as a screening test.
FUNDING Bowel Disease Research Foundation.

Output: True

Input: Consider Input: Purpose. To demonstrate how correlations among predictor variables in a population pharmacokinetic model affect the ability to discern which covariates should enter into the structural pharmacokinetic model. Methods. Monte Carlo simulation was used to generate multiple-dose concentration-time data similar to that seen in a Phase III clinical trial. The drugs' pharmacokinetics were dependent on two covariates. Five data sets were simulated with increasing correlation between the two covariates. All data sets were analyzed using NONMEM both with and without inclusion of the covariates in the structural pharmacokinetic model. Summary measures for ill-conditioning and sensitivity analysis were used to examine how increasing correlation among covariates affects the accuracy and precision of the parameter estimates. Results. When covariates were included in the structural pharmacokinetic model and the correlation between covariates increased, the standard error of the parameter estimates increased and the value of parameter estimates themselves became increasingly biased. When the correlation between predictor variables was 0.75, the standard errors of the parameter estimates were too large to declare statistical significance. Conclusions. Correlations among predictor variables greater than 0.5 when entered into the model simultaneously should be a warning to researchers because the (1) the accuracy of the parameter estimates themselves may be biased and (2) the precision of the estimates may be inflated due to ill-conditioning.
Output: False