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Nam-Gu Her

Researcher at Samsung Medical Center

Publications -  14
Citations -  143

Nam-Gu Her is an academic researcher from Samsung Medical Center. The author has contributed to research in topics: Cancer & Medicine. The author has an hindex of 5, co-authored 11 publications receiving 65 citations.

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Potent effect of the MDM2 inhibitor AMG232 on suppression of glioblastoma stem cells

TL;DR: The data provide new evidence that glioblastoma stem cells have high susceptibility to AMG232 suggesting the potential clinical implications of MDM2 inhibition for gliOBlastoma treatment, andAMG232 is highly efficacious in three-dimensional tumor spheroids growth and effectively inhibits the stemness-related factors, Nestin and ZEB1.
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WNT Signaling as a Therapeutic Target for Glioblastoma

TL;DR: In this article, the authors deeply scrutinize the WNT signaling pathway and its involvement in the genesis of glioblastoma as well as its acquired therapy resistance, and provide an analysis of the pathway in terms of its therapeutic importance in addition to an overview of the current targeted therapies under clinical investigation.
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The Protein Neddylation Inhibitor MLN4924 Suppresses Patient-Derived Glioblastoma Cells via Inhibition of ERK and AKT Signaling.

TL;DR: The findings suggest that patient-derived glioblastoma stem cells in the context of ERK and AKT activation are sensitive and highly regulated by neddylation inhibition.
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Allosteric inhibitor of β-catenin selectively targets oncogenic Wnt signaling in colon cancer.

TL;DR: C2, a novel β-catenin inhibitor, is identified, which is a small molecule that binds to a novel allosteric site on the surface of β- catenin that renders the target inactive that eventually activates proteasome system for its removal.
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Accuracy improvement of quantification information using super-resolution with convolutional neural network for microscopy images

TL;DR: A novel method to improve quantification accuracy using a super-resolution with a convolutional neural network (CNN) with image-based cell phenotypic profiling to predict the responses of glioblastoma cells to a drug using automatic image processing is proposed.