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: Metagenomics is currently the only generic method for pathogen detection. Starting from RNA allows the assessment of the whole sample community including RNA viruses. Here we present our modular concerted protocol for sample processing for diagnostic metagenomics analysis of human, animal, and food samples. The workflow does not rely on dedicated amplification steps at any stage in the process and, in contrast to published methods, libraries prepared accordingly will yield only minute amounts of unclassifiable reads. We confirmed the performance of the approach using a spectrum of pathogen/ matrix-combinations showing it has the potential to become a commonly usable analytical framework. Diagnostic metagenomics with high-throughput sequencing (HTS) techniques continuously gains importance for broad and swift identification of pathogens in human, animal, and food samples 1 . While for known pathogens, highly sensitive and specific diagnostic methods like real-time quantitative PCR (qPCR) are in routine use and deliver reliable results, the identification of unrecognized pathogens, meaning unexpected or newly emerging pathogens or pathogens that are only distantly related with known ones, can be very difficult. In this respect, metagenomics using HTS are much more promising than routine diagnostics. Unrecognized pathogens, especially newly emerging zoonoses, may cause serious infectious diseases, and a delay of medical treatment or development of vaccines might have fatal consequences for the affected patients and animal stocks. Such delays can be caused by performing numerous laborious screening tests until the potential pathogen is found instead of a single comprehensive screening test. Prominent cases of emerging infectious diseases caused by novel or varying viruses for instance are the discovery of the Middle East respiratory syndrome (MERS) coronavirus in 2012 2 , the tremendous Ebola outbreak in 2014 3 , the report on a novel zoonotic bornavirus 4 , or the detection of the new Schmallenberg virus 5 affecting domestic and wild ruminants. However, infectious diseases with clinical signs like high fever, diarrhoea, or encephalitis − often life-threatening − can be caused by very different infectious agents 6,7 , not only viruses. In such puzzling cases, a generic approach that works likewise successful and efficient for all pathogen groups, as sketched in Fig. 1 , is essential. In a number of review articles 1,8-12 , valuable considerations for this approach have been summarized. In addition to the overall workflow outlined in Fig. 1 , after metagenomic analysis, it is desirable to confirm the initial sequencing-based suspicion by other methods and in the ideal case by fulfilling the Henle-Loeffler-Koch postulates 13 as for example done in case of the Schmallenberg virus 5 . Metagenomics for generic pathogen detection, so-called diagnostic metagenomics, in its pure form is a broad and undirected approach to find gene sequences or sequence fragments of infectious agents within sequence data sets of the whole community of a sample generated by high-throughput sequencing. A crucial point when handling diagnostic sequencing approaches is to explicitly distinguish between i) high-throughput well-standardisable routine diagnostics for expected known pathogens and ii) diagnostic metagenomics for all including unrecognized pathogens. The first approach is easier to design based on the spectrum of known sequences, testing with specific oligonucleotide primers for pathogens of known identity 14,15 , known tropism, and maybe also of a known proportion within a sample. The latter one − used for unrecognized pathogens like in the case of Schmallenberg virus 5 − should be a generic approach that can ideally be applied to all samples and pathogens. This approach can indeed be developed and tested using samples of known origin and pathogen content. In case of an emergency, however, there might be no information about nature and proportion of the pathogen and only clinical data could be available, and the sample is a closed book. For these cases, metagenomics Published: xx xx xxxx OPEN www.nature.com/scientificreports/ 
 Title: A Versatile Sample Processing Workflow for Metagenomic Pathogen Detection OPEN
False

Paragraph: Antibody-based therapeutics currently enjoy unprecedented success, growth in research and revenues, and recognition of their potential. It appears that the promise of the "magic bullet" has largely been realized. There are currently 22 monoclonal antibodies (mAbs) approved by the United States Food and Drug Administration (FDA) for clinical use and hundreds are in clinical trials for treatment of various diseases including cancers, immune disorders, and infections. The revenues from the top five therapeutic antibodies (Rituxan, Remicade, Herceptin, Humira, and Avastin) nearly doubled from $6.4 billion in 2004 to $11.7 billion in 2006. During the last several years major pharmaceutical companies raced to acquire antibody companies, with a recent example of MedImmune being purchased for $15.6 billion by AstraZeneca. These therapeutic and business successes reflect the major advances in antibody engineering which have resulted in the generation of safe, specific, high-affinity, and non-immunogenic antibodies during the last three decades. Currently, second and third generations of antibodies are under development, mostly to improve already existing antibody specificities. However, although the refinement of already known methodologies is certainly of great importance for potential clinical use, there are no conceptually new developments in the last decade comparable, for example, to the development of antibody libraries, phage display, domain antibodies (dAbs), and antibody humanization to name a few. A fundamental question is then whether there will be another change in the paradigm of research as happened 1-2 decades ago or the current trend of gradual improvement of already developed methodologies and therapeutic antibodies will continue. Although any prediction could prove incorrect, it appears that conceptually new methodologies are needed to overcome the fundamental problems of drug (antibody) resistance due to genetic or/and epigenetic alterations in cancer and chronic infections, as well as problems related to access to targets and complexity of biological systems. If new methodologies are not developed, it is likely that gradual saturation will occur in the pipeline of conceptually new antibody therapeutics. In this scenario we will witness an increase in combination of targets and antibodies, and further attempts to personalize targeted treatments by using appropriate biomarkers as well as to develop novel scaffolds with properties that are superior to those of the antibodies now in clinical use. 
 Title: Therapeutic Antibodies: Current State and Future Trends -Is a Paradigm Change Coming Soon?

False