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Full-Text Articles in Medicine and Health Sciences
Voltage-Dependent Gating Rearrangements In The Intracellular T1-T1 Interface Of A K+ Channel., Guangyu Wang, Manuel Covarrubias
Voltage-Dependent Gating Rearrangements In The Intracellular T1-T1 Interface Of A K+ Channel., Guangyu Wang, Manuel Covarrubias
Department of Pathology, Anatomy, and Cell Biology Faculty Papers
The intracellular tetramerization domain (T1) of most eukaryotic voltage-gated potassium channels (Kv channels) exists as a "hanging gondola" below the transmembrane regions that directly control activation gating via the electromechanical coupling between the S4 voltage sensor and the main S6 gate. However, much less is known about the putative contribution of the T1 domain to Kv channel gating. This possibility is mechanistically intriguing because the T1-S1 linker connects the T1 domain to the voltage-sensing domain. Previously, we demonstrated that thiol-specific reagents inhibit Kv4.1 channels by reacting in a state-dependent manner with native Zn(2+) site thiolate groups in the T1-T1 interface; …
Classification And Risk Stratification Of Invasive Breast Carcinomas Using A Real-Time Quantitative Rt-Pcr Assay., Laurent Perreard, Cheng Fan, John F Quackenbush, Michael Mullins, Nicholas P Gauthier, Edward Nelson, Mary Mone, Heidi Hansen, Saundra S Buys, Karen Rasmussen, Alejandra Ruiz Orrico, Donna Dreher, Rhonda Walters, Joel Parker, Zhiyuan Hu, Xiaping He, Juan P Palazzo, Olufunmilayo I Olopade, Aniko Szabo, Charles M Perou, Philip S Bernard
Classification And Risk Stratification Of Invasive Breast Carcinomas Using A Real-Time Quantitative Rt-Pcr Assay., Laurent Perreard, Cheng Fan, John F Quackenbush, Michael Mullins, Nicholas P Gauthier, Edward Nelson, Mary Mone, Heidi Hansen, Saundra S Buys, Karen Rasmussen, Alejandra Ruiz Orrico, Donna Dreher, Rhonda Walters, Joel Parker, Zhiyuan Hu, Xiaping He, Juan P Palazzo, Olufunmilayo I Olopade, Aniko Szabo, Charles M Perou, Philip S Bernard
Department of Pathology, Anatomy, and Cell Biology Faculty Papers
INTRODUCTION: Predicting the clinical course of breast cancer is often difficult because it is a diverse disease comprised of many biological subtypes. Gene expression profiling by microarray analysis has identified breast cancer signatures that are important for prognosis and treatment. In the current article, we use microarray analysis and a real-time quantitative reverse-transcription (qRT)-PCR assay to risk-stratify breast cancers based on biological 'intrinsic' subtypes and proliferation. METHODS: Gene sets were selected from microarray data to assess proliferation and to classify breast cancers into four different molecular subtypes, designated Luminal, Normal-like, HER2+/ER-, and Basal-like. One-hundred and twenty-three breast samples (117 invasive …