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10.1097/CAD.0000000000000075 http://scihub22266oqcxt.onion/10.1097/CAD.0000000000000075 C4010870!4010870!24418909     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Anticancer+Drugs 2014 ; 25 (4): 353-67 Nephropedia Template TP {{tp|p=24418909|t=2014. Cancer Stem Cells: A systems biology view of their role in prognosis and therapy |pdf=|usr=}}{{24418909}} gab.com Text Cancer Stem Cells: A systems biology view of their role in prognosis and therapy JO: Anticancer Drugs http://www.kidney.de/pget.php?&tw=1&pmid=24418909 #FOAvip Evidence has accumulated that characterizes highly tumorigenic cancer cells residing in heterogeneous populations. The accepted term for such a subpopulation is cancer stem cells (CSC). While many questions still remain about their precise role in the origin, progression, and drug resistance of tumors, it is clear they exist. In this review, a current understanding of the nature of CSC, their potential usefulness in prognosis, and the need to target them will be discussed. In particular, separate studies now suggest that the CSC is plastic in its phenotype, toggling between tumorigenic and nontumorigenic states depending on both intrinsic and extrinsic conditions. Because of this, a static view of gene and protein levels defined by correlations may not be sufficient to either predict disease progression or aid in the discovery and development of drugs to molecular targets leading to cures. Quantitative dynamic modeling, a bottom up systems biology approach whereby signal transduction pathways are described by differential equations, may offer a novel means to overcome the challenges of oncology today. In conclusion, the complexity of cancer stem cells can be captured in mathematical models that may be useful for selecting molecular targets, defining drug action, and predicting sensitivity or resistance pathways for improved patient outcomes. Twit Text FOAVip Cancer Stem Cells: A systems biology view of their role in prognosis and therapy ????Anticancer Drugs http://www.kidney.de/pget.php?&tw=1&pmid=24418909 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24418909 #FOAvip English Wikipedia <ref>{{Cite pmid|24418909}}</ref> Cancer Stem Cells: A systems biology view of their role in prognosis and therapy #MMPMID24418909 Mertins SD Anticancer Drugs 2014[Apr]; 25 (4): 353-67 PMID24418909show ga Evidence has accumulated that characterizes highly tumorigenic cancer cells residing in heterogeneous populations. The accepted term for such a subpopulation is cancer stem cells (CSC). While many questions still remain about their precise role in the origin, progression, and drug resistance of tumors, it is clear they exist. In this review, a current understanding of the nature of CSC, their potential usefulness in prognosis, and the need to target them will be discussed. In particular, separate studies now suggest that the CSC is plastic in its phenotype, toggling between tumorigenic and nontumorigenic states depending on both intrinsic and extrinsic conditions. Because of this, a static view of gene and protein levels defined by correlations may not be sufficient to either predict disease progression or aid in the discovery and development of drugs to molecular targets leading to cures. Quantitative dynamic modeling, a bottom up systems biology approach whereby signal transduction pathways are described by differential equations, may offer a novel means to overcome the challenges of oncology today. In conclusion, the complexity of cancer stem cells can be captured in mathematical models that may be useful for selecting molecular targets, defining drug action, and predicting sensitivity or resistance pathways for improved patient outcomes. äCancer Stem Cells: A systems biology view of their role in prognosis a(epmc).pdf DeepDyve Pubget Overpricing