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| http://dx.doi.org/10.21037/tgh.2019.05.09 | DOI Listing |
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Robust Bayesian graphical regression models for assessing tumor heterogeneity in proteomic networks.
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Department of Biostatistics, University of Michigan at Ann Arbor, Ann Arbor, MI 48109, United States.
Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of two canonical assumptions: (i) a homogeneous graph with a common network for all subjects or (ii) an assumption of normality, especially in the context of Gaussian graphical models. Both assumptions are restrictive and can fail to hold in certain applications such as proteomic networks in cancer.
View Article and Find Full Text PDFTowards measurements of absolute membrane potential in Bacillus subtilis using fluorescence lifetime.
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UCLA-DOE Institute for Genomics and Proteomics, Department of Biological Chemistry, University of California at Los Angeles, Los Angeles, CA 90095, USA,; Department of Chemistry and Biochemistry, University of California at Los Angeles, Los Angeles, CA 90095, USA,; Department of Physiology, University of California at Los Angeles, Los Angeles, CA 90095, USA,; California Nano Systems Institute, University of California at Los Angeles, Los Angeles, CA 90095, USA,; Department of Physics, Institute for Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan 52900, Israel.
Membrane potential (MP) changes can provide a simple readout of bacterial functional and metabolic state or stress levels. While several optical methods exist for measuring fast changes in MP in excitable cells, there is a dearth of such methods for absolute and precise measurements of steady-state membrane potentials (MPs) in bacterial cells. Conventional electrode-based methods for the measurement of MP are not suitable for calibrating optical methods in small bacterial cells.
View Article and Find Full Text PDFPreclinical Models for Functional Precision Lung Cancer Research.
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Division of Hematology/Oncology, Department of Internal Medicine, University of California Davis School of Medicine, University of California Davis Comprehensive Cancer Center, Sacramento, CA 95817, USA.
Patient-centered precision oncology strives to deliver individualized cancer care. In lung cancer, preclinical models and technological innovations have become critical in advancing this approach. Preclinical models enable deeper insights into tumor biology and enhance the selection of appropriate systemic therapies across chemotherapy, targeted therapies, immunotherapies, antibody-drug conjugates, and emerging investigational treatments.
View Article and Find Full Text PDFPeroxidasin is associated with a mesenchymal-like transcriptional phenotype and promotes invasion in metastatic melanoma.
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Mātai Hāora, Centre for Redox Biology and Medicine, University of Otago Christchurch, Christchurch, New Zealand. Electronic address:
Cutaneous melanoma is a highly invasive, heterogeneous and treatment resistant cancer. It's ability to dynamically shift between transcriptional states or phenotypes results in an adaptive cell plasticity that may drive cancer cell invasion or the development of therapy resistance. The expression of peroxidasin (PXDN), an extracellular matrix peroxidase, has been proposed to be associated with the invasive metastatic melanoma phenotype.
View Article and Find Full Text PDFPurpose: In glioblastoma, the therapeutically intractable and resistant phenotypes can be derived from glioma stem cells, which often have different underlying mechanisms from non-stem glioma cells. Aberrant signaling across the EGFR-PTEN-AKT-mTOR pathways have been shown as common drivers of glioblastoma. Revealing the inter and intra-cellular heterogeneity within glioma stem cell populations in relations to signaling patterns through these pathways may be key to precision diagnostic and therapeutic targeting of these cells.
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