In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

Paragraph: CD133 (prominin-1), a 5-transmembrane glycoprotein, has recently been considered to be an important marker that represents the subset population of cancer stem-like cells. Herein we report the isolation of CD133-positive cells (LC-CD133 + ) and CD133-negative cells (LC-CD133 2 ) from tissue samples of ten patients with non-small cell lung cancer (LC) and five LC cell lines. LC-CD133 + displayed higher Oct-4 expressions with the ability to self-renew and may represent a reservoir with proliferative potential for generating lung cancer cells. Furthermore, LC-CD133 + , unlike LC-CD133 2 , highly coexpressed the multiple drug-resistant marker ABCG2 and showed significant resistance to chemotherapy agents (i.e., cisplatin, etoposide, doxorubicin, and paclitaxel) and radiotherapy. The treatment of Oct-4 siRNA with lentiviral vector can specifically block the capability of LC-CD133 + to form spheres and can further facilitate LC-CD133 + to differentiate into LC-CD133 2 . In addition, knock-down of Oct-4 expression in LC-CD133 + can significantly inhibit the abilities of tumor invasion and colony formation, and increase apoptotic activities of caspase 3 and poly (ADP-ribose) polymerase (PARP). Finally, in vitro and in vivo studies further confirm that the treatment effect of chemoradiotherapy for LC-CD133 + can be improved by the treatment of Oct-4 siRNA. In conclusion, we demonstrated that Oct-4 expression plays a crucial role in maintaining the self-renewing, cancer stem-like, and chemoradioresistant properties of LC-CD133 + . Future research is warranted regarding the up-regulated expression of Oct-4 in LC-CD133 + and malignant lung cancer. 
 Title: Oct-4 Expression Maintained Cancer Stem-Like Properties in Lung Cancer-Derived CD133-Positive Cells
True

Paragraph: Madin Darby canine kidney (MDCK) cells were adapted to serum-free RPMI 1640 medium and used for cultivation of canine viruses. RPMI 1640 medium was supplemented with a soybean peptone, l-glutamine and antibiotics, so that the protein concentration was less than 5 g/ml (RPMI/SP medium). The resulting adapted MDCK-SP cells showed steady growth after the twenty-eighth passage in RPMI/SP medium (MDCK-SP cell culture). Canine distemper virus, canine parvovirus, canine adenoviruses and canine parainfluenza virus, which are the principal components of canine combined virus vaccines, grew in the MDCK-SP cell culture as efficiently as the parental MDCK cells cultured in the conventional Eagle's MEM containing fetal bovine serum. Consequently, the use of MDCK-SP cell culture can make current canine vaccine products much safer, of higher quality and at lower cost. 
 Title: Growth characteristics of canine pathogenic viruses in MDCK cells cultured in RPMI 1640 medium without animal protein
True

Paragraph: Motivation: Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. Results: We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. Availability and implementation: metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix Contact: sofia.morfopoulou.10@ucl.ac.uk Supplementary information: Supplementary data are available at Bionformatics online. 
 Title: Bayesian mixture analysis for metagenomic community profiling

False