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David J. Fischer

Researcher at Merck Serono

Publications -  33
Citations -  2538

David J. Fischer is an academic researcher from Merck Serono. The author has contributed to research in topics: Receptor & Lysophosphatidic acid. The author has an hindex of 22, co-authored 33 publications receiving 2448 citations. Previous affiliations of David J. Fischer include University of Kansas & University of Tennessee Health Science Center.

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Recent developments in electrochemical detection for microchip capillary electrophoresis

TL;DR: This review focuses on recent developments in integrating electrochemical (EC) detection with microchip capillary electrophoresis (CE) and applications for enzyme/immunoassays, clinical and environmental assays, as well as the detection of neurotransmitters.
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Structure of follicle-stimulating hormone in complex with the entire ectodomain of its receptor.

TL;DR: The crystal structure of FSH in complex with the entire extracellular domain of F SHR (FSHRED), including the enigmatic hinge region that is responsible for signal specificity, suggests FSHR interacts with its ligand in two steps: ligand recruitment followed by sTyr recognition.
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Lysophosphatidic Acid-Induced Neurite Retraction in PC12 Cells: Control by Phosphoinositide-Ca2+ Signaling and Rho

TL;DR: Although activation of the PIP‐Ca2+ second messenger system appears necessary for the Rho‐mediated rearrangements of the actin cytoskeleton, bradykinin failed to cause retraction, indicating that a yet unidentified novel mechanism is also involved in the LPA‐induced morphoregulatory response.
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Molecular cloning of a high-affinity receptor for the growth factor-like lipid mediator lysophosphatidic acid from Xenopus oocytes

TL;DR: The identification of a complementary DNA from Xenopus that encodes a functional high-affinity LPA receptor is reported, which is a potent regulator of mammalian cell proliferation and one of the major mitogens found in blood serum.
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Identification of Edg1 receptor residues that recognize sphingosine 1-phosphate.

TL;DR: This computational map of the ligand binding pocket provides information necessary for understanding the molecular pharmacology of this receptor, thus underlining the potential of the computational method in predicting ligand-receptor interactions.