Titlebook: Immunosuppressant Analogs in Neuroprotection; Cesario V. Borlongan,Ole Isacson,Paul R. Sanberg Book 2003 Springer Science+Business Media N

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書目名稱Immunosuppressant Analogs in Neuroprotection被引頻次學科排名




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書目名稱Immunosuppressant Analogs in Neuroprotection年度引用學科排名




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書目名稱Immunosuppressant Analogs in Neuroprotection讀者反饋學科排名

BYRE

Effects of Neuroimmunophilin Ligands on Parkinson’s Disease and Cognitionein of 12 kDa, (FKBP12) . Each of these immunophilins has an inherent enzymatic activity, designated protein foldase, peptidyl prolyl-.-isomerase or rotamase activity . This enzyme acts to rotate X-prolyl bonds in protein substrates by 180° from the . to the . conformer. Binding of each immunosuppre

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Cyclosporin A Protects Striatal Neurons from Mitochondrial Dysfunctionon of this protein may be involved in the pathogenesis of HD .. Recently, CAG expansion has been shown to activate caspase-8, a known trigger of apoptotic cell death .. However, the specific mechanism(s) by which the polyglutamine expansion damages and ultimately destroys neurons remains to be estab

Genistein

Inhibition of Lipid Peroxidation by Cyclosporin After Spinal Cord Injury in Ratsrotective effect of CsA may be the result of several mechanisms, and among them, lipid peroxidation (LP) inhibition may play an important role .. Recently, we reported on the inhibitory effect of CsA upon LP in a spinal cord (SC) injury model ..

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PLAYS

Antonio Ibarra,Araceli Diaz-Ruizs of CCT are all covered in detail, and in addition relevant information is provided on epidemiology, clinical assessment, and the role of other diagnostic modalities. This book will prove an invaluable tool fo978-88-470-3902-5978-88-470-2522-6

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Book 2003 vivo; using these models of CNS disease, they report laboratory studies with compelling clinical indications for Alzheimer‘s disease, Huntington‘s disease, stroke, traumatic brain injury, spinal cord injury, ALS, sciatic nerve injury, and drug addiction. They also present novel hypotheses on the me

ADORN

rm in real applications. For each of the performance measures, we discuss the optimal values that can be achieved by a given model and what should be considered as acceptable. We also show the relationship between the different measures, which can give more insights when interpreting the results of

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ion, population health surveillance, data representation and integration, and clinical trial support. This history provides the reader with an “family tree” of sorts that shows the evolution of artificial intelligence through the past seven decades and its application to medicine and public health.