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10.1007/s12609-014-0143-2 http://scihub22266oqcxt.onion/10.1007/s12609-014-0143-2 C4039558!4039558!24891927     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Curr+Breast+Cancer+Rep 2014 ; 6 (2): 110-20 Nephropedia Template TP {{tp|p=24891927|t=2014. Understanding Drug Resistance in Breast Cancer with Mathematical Oncology |pdf=|usr=}}{{24891927}} gab.com Text Understanding Drug Resistance in Breast Cancer with Mathematical Oncology JO: Curr Breast Cancer Rep http://www.kidney.de/pget.php?&tw=1&pmid=24891927 #FOAvip Chemotherapy is mainstay of treatment for the majority of patients with breast cancer, but results in only 26% of patients with distant metastasis living 5 years past treatment in the United States, largely due to drug resistance. The complexity of drug resistance calls for an integrated approach of mathematical modeling and experimental investigation to develop quantitative tools that reveal insights into drug resistance mechanisms, predict chemotherapy efficacy, and identify novel treatment approaches. This paper reviews recent modeling work for understanding cancer drug resistance through the use of computer simulations of molecular signaling networks and cancerous tissues, with a particular focus on breast cancer. These mathematical models are developed by drawing on current advances in molecular biology, physical characterization of tumors, and emerging drug delivery methods (e.g., nanotherapeutics). We focus our discussion on representative modeling works that have provided quantitative insight into chemotherapy resistance in breast cancer and how drug resistance can be overcome or minimized to optimize chemotherapy treatment. We also discuss future directions of mathematical modeling in understanding drug resistance. Twit Text FOAVip Understanding Drug Resistance in Breast Cancer with Mathematical Oncology ????Curr Breast Cancer Rep http://www.kidney.de/pget.php?&tw=1&pmid=24891927 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24891927 #FOAvip English Wikipedia <ref>{{Cite pmid|24891927}}</ref> Understanding Drug Resistance in Breast Cancer with Mathematical Oncology #MMPMID24891927 Brocato T; Dogra P; Koay EJ; Day A; Chuang YL; Wang Z; Cristini V Curr Breast Cancer Rep 2014[Jun]; 6 (2): 110-20 PMID24891927show ga Chemotherapy is mainstay of treatment for the majority of patients with breast cancer, but results in only 26% of patients with distant metastasis living 5 years past treatment in the United States, largely due to drug resistance. The complexity of drug resistance calls for an integrated approach of mathematical modeling and experimental investigation to develop quantitative tools that reveal insights into drug resistance mechanisms, predict chemotherapy efficacy, and identify novel treatment approaches. This paper reviews recent modeling work for understanding cancer drug resistance through the use of computer simulations of molecular signaling networks and cancerous tissues, with a particular focus on breast cancer. These mathematical models are developed by drawing on current advances in molecular biology, physical characterization of tumors, and emerging drug delivery methods (e.g., nanotherapeutics). We focus our discussion on representative modeling works that have provided quantitative insight into chemotherapy resistance in breast cancer and how drug resistance can be overcome or minimized to optimize chemotherapy treatment. We also discuss future directions of mathematical modeling in understanding drug resistance. äUnderstanding Drug Resistance in Breast Cancer with Mathematical Oncol(epmc).pdf DeepDyve Pubget Overpricing